role of problem solving in programming

What Is Problem Solving? How Software Engineers Approach Complex Challenges

From debugging an existing system to designing an entirely new software application, a day in the life of a software engineer is filled with various challenges and complexities. The one skill that glues these disparate tasks together and makes them manageable? Problem solving . 

Throughout this blog post, we’ll explore why problem-solving skills are so critical for software engineers, delve into the techniques they use to address complex challenges, and discuss how hiring managers can identify these skills during the hiring process. 

What Is Problem Solving?

But what exactly is problem solving in the context of software engineering? How does it work, and why is it so important?

Problem solving, in the simplest terms, is the process of identifying a problem, analyzing it, and finding the most effective solution to overcome it. For software engineers, this process is deeply embedded in their daily workflow. It could be something as simple as figuring out why a piece of code isn’t working as expected, or something as complex as designing the architecture for a new software system. 

In a world where technology is evolving at a blistering pace, the complexity and volume of problems that software engineers face are also growing. As such, the ability to tackle these issues head-on and find innovative solutions is not only a handy skill — it’s a necessity. 

The Importance of Problem-Solving Skills for Software Engineers

Problem-solving isn’t just another ability that software engineers pull out of their toolkits when they encounter a bug or a system failure. It’s a constant, ongoing process that’s intrinsic to every aspect of their work. Let’s break down why this skill is so critical.

Driving Development Forward

Without problem solving, software development would hit a standstill. Every new feature, every optimization, and every bug fix is a problem that needs solving. Whether it’s a performance issue that needs diagnosing or a user interface that needs improving, the capacity to tackle and solve these problems is what keeps the wheels of development turning.

It’s estimated that 60% of software development lifecycle costs are related to maintenance tasks, including debugging and problem solving. This highlights how pivotal this skill is to the everyday functioning and advancement of software systems.

Innovation and Optimization

The importance of problem solving isn’t confined to reactive scenarios; it also plays a major role in proactive, innovative initiatives . Software engineers often need to think outside the box to come up with creative solutions, whether it’s optimizing an algorithm to run faster or designing a new feature to meet customer needs. These are all forms of problem solving.

Consider the development of the modern smartphone. It wasn’t born out of a pre-existing issue but was a solution to a problem people didn’t realize they had — a device that combined communication, entertainment, and productivity into one handheld tool.

Increasing Efficiency and Productivity

Good problem-solving skills can save a lot of time and resources. Effective problem-solvers are adept at dissecting an issue to understand its root cause, thus reducing the time spent on trial and error. This efficiency means projects move faster, releases happen sooner, and businesses stay ahead of their competition.

Improving Software Quality

Problem solving also plays a significant role in enhancing the quality of the end product. By tackling the root causes of bugs and system failures, software engineers can deliver reliable, high-performing software. This is critical because, according to the Consortium for Information and Software Quality, poor quality software in the U.S. in 2022 cost at least $2.41 trillion in operational issues, wasted developer time, and other related problems.

Problem-Solving Techniques in Software Engineering

So how do software engineers go about tackling these complex challenges? Let’s explore some of the key problem-solving techniques, theories, and processes they commonly use.

Decomposition

Breaking down a problem into smaller, manageable parts is one of the first steps in the problem-solving process. It’s like dealing with a complicated puzzle. You don’t try to solve it all at once. Instead, you separate the pieces, group them based on similarities, and then start working on the smaller sets. This method allows software engineers to handle complex issues without being overwhelmed and makes it easier to identify where things might be going wrong.

Abstraction

In the realm of software engineering, abstraction means focusing on the necessary information only and ignoring irrelevant details. It is a way of simplifying complex systems to make them easier to understand and manage. For instance, a software engineer might ignore the details of how a database works to focus on the information it holds and how to retrieve or modify that information.

Algorithmic Thinking

At its core, software engineering is about creating algorithms — step-by-step procedures to solve a problem or accomplish a goal. Algorithmic thinking involves conceiving and expressing these procedures clearly and accurately and viewing every problem through an algorithmic lens. A well-designed algorithm not only solves the problem at hand but also does so efficiently, saving computational resources.

Parallel Thinking

Parallel thinking is a structured process where team members think in the same direction at the same time, allowing for more organized discussion and collaboration. It’s an approach popularized by Edward de Bono with the “ Six Thinking Hats ” technique, where each “hat” represents a different style of thinking.

In the context of software engineering, parallel thinking can be highly effective for problem solving. For instance, when dealing with a complex issue, the team can use the “White Hat” to focus solely on the data and facts about the problem, then the “Black Hat” to consider potential problems with a proposed solution, and so on. This structured approach can lead to more comprehensive analysis and more effective solutions, and it ensures that everyone’s perspectives are considered.

This is the process of identifying and fixing errors in code . Debugging involves carefully reviewing the code, reproducing and analyzing the error, and then making necessary modifications to rectify the problem. It’s a key part of maintaining and improving software quality.

Testing and Validation

Testing is an essential part of problem solving in software engineering. Engineers use a variety of tests to verify that their code works as expected and to uncover any potential issues. These range from unit tests that check individual components of the code to integration tests that ensure the pieces work well together. Validation, on the other hand, ensures that the solution not only works but also fulfills the intended requirements and objectives.

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Evaluating Problem-Solving Skills

We’ve examined the importance of problem-solving in the work of a software engineer and explored various techniques software engineers employ to approach complex challenges. Now, let’s delve into how hiring teams can identify and evaluate problem-solving skills during the hiring process.

Recognizing Problem-Solving Skills in Candidates

How can you tell if a candidate is a good problem solver? Look for these indicators:

• Previous Experience: A history of dealing with complex, challenging projects is often a good sign. Ask the candidate to discuss a difficult problem they faced in a previous role and how they solved it.
• Problem-Solving Questions: During interviews, pose hypothetical scenarios or present real problems your company has faced. Ask candidates to explain how they would tackle these issues. You’re not just looking for a correct solution but the thought process that led them there.
• Technical Tests: Coding challenges and other technical tests can provide insight into a candidate’s problem-solving abilities. Consider leveraging a platform for assessing these skills in a realistic, job-related context.

Assessing Problem-Solving Skills

Once you’ve identified potential problem solvers, here are a few ways you can assess their skills:

• Solution Effectiveness: Did the candidate solve the problem? How efficient and effective is their solution?
• Approach and Process: Go beyond whether or not they solved the problem and examine how they arrived at their solution. Did they break the problem down into manageable parts? Did they consider different perspectives and possibilities?
• Communication: A good problem solver can explain their thought process clearly. Can the candidate effectively communicate how they arrived at their solution and why they chose it?
• Adaptability: Problem-solving often involves a degree of trial and error. How does the candidate handle roadblocks? Do they adapt their approach based on new information or feedback?

Hiring managers play a crucial role in identifying and fostering problem-solving skills within their teams. By focusing on these abilities during the hiring process, companies can build teams that are more capable, innovative, and resilient.

Key Takeaways

As you can see, problem solving plays a pivotal role in software engineering. Far from being an occasional requirement, it is the lifeblood that drives development forward, catalyzes innovation, and delivers of quality software. 

By leveraging problem-solving techniques, software engineers employ a powerful suite of strategies to overcome complex challenges. But mastering these techniques isn’t simple feat. It requires a learning mindset, regular practice, collaboration, reflective thinking, resilience, and a commitment to staying updated with industry trends. 

For hiring managers and team leads, recognizing these skills and fostering a culture that values and nurtures problem solving is key. It’s this emphasis on problem solving that can differentiate an average team from a high-performing one and an ordinary product from an industry-leading one.

At the end of the day, software engineering is fundamentally about solving problems — problems that matter to businesses, to users, and to the wider society. And it’s the proficient problem solvers who stand at the forefront of this dynamic field, turning challenges into opportunities, and ideas into reality.

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A Guide to Problem-Solving for Software Developers with Examples

If I ask you, out of the blue, what’s the role of a developer, what would you answer? Coding all day? Drinking coffee? Complaining about the management?

To me, a developer is first and foremost a problem solver, simply because solving problem is the most important (and the most difficult) part of our job. After all, even if our code is perfect, clear, performing great, a masterpiece of form and meaning, it’s useless if it doesn’t solve the problem it was meant to solve.

So, let’s dive into problem-solving today. More specifically, we’ll see in this article:

• How to define a problem, and the difference sometimes made between problem-solving and decision-making.
• Why some problems should not be solved.
• The two wide categories of problems you can encounter.
• Why it’s important to correctly define the problem, and how to do so.
• How to explore the solution space.
• Why deferring a problem might be the best decision to make in specific situations.
• Why reflecting on the whole process afterward can help you in the future.

This article is mostly based on my own experience, even if I apply here some ideas I found in books and papers.

We have our plan. Now, it’s time to dive deep into the difficult, but rewarding, process of problem-solving.

Problem-Solving and Decision-Making

“When I use a word,” Humpty Dumpty said in rather a scornful tone, “it means just what I choose it to mean — neither more nor less.” “The question is,” said Alice, “whether you can make words mean so many different things.” “The question is,” said Humpty Dumpty, “which is to be master — that’s all.” Lewis Caroll Source

Words are ambiguous; they can mean different things for each of us. So let’s first begin to agree on the definition of “problem-solving” here, to be sure we’re on the same page.

Let’s first look at the definition of the word “problem” in a dictionary:

• According to the American Heritage Dictionary , a problem is “a question to be considered, solved, or answered”.
• According to the Oxford Learner’s dictionary , a problem is “a thing that is difficult to deal with or to understand”.

In short, in any problem, there is some degree of uncertainty. If you’re certain of the solution, the problem is already solved. Nothing would need to be “considered, solved, or answered”.

Information is useful to reduce this uncertainty. The quantity is often not the most important, but the quality will be decisive. If I tell you that 90% of my readers are extremely intelligent, would it help you to solve a problem in your daily job? I bet it wouldn’t. It’s information nonetheless, but its usefulness for you is close to zero.

This is an extreme example, but it highlights an important point: before collecting any data, define your problem clearly; then, according to the problem, decide what data you need. Yet, many companies out there begin to collect the data and then decide what problem to solve. We’ll come back to that soon in this article.

So, to summarize, a problem is a situation with some degree of uncertainty. Sometimes, this uncertainty needs to be reduced to come up with an appropriate solution, or, at least, a decision to move forward to your specific goal.

Is there a Problem to Solve?

Whenever you (or somebody else) see a problem, you should always ask yourself this simple question first: is it really a problem, and should we solve it now ?

In other words, ask yourself the following questions:

• Why is this problem important to solve?
• Would be solving the problem creates some value? What value?
• What would happen if the problem was not solved?
• What desired outcome do we expect by solving the problem?

If the problem doesn’t bother anybody and solving it doesn’t create any value, why allocating effort and time to solve it?

It sounds obvious, but it’s an important point nonetheless. More often than not, I see developers heading first in solving problems without asking themselves if they should solve them at the first place.

The most common examples I can think of are useless refactoring. I saw developers refactoring parts of codebases which never change, or is rarely executed at runtime. In the mind of the developer, the code itself is the problem: refactoring is the solution.

I remember a similar case: a developer refactored part of the codebase which was basically never used. We discovered, months later, when we had more and more users using this specific part of the codebase, that the refactoring didn’t really simplify anything. To the contrary; we had to refactor the code again. The first refactoring tried to solve a problem which didn’t exists.

Of course, the developer could argue that the value created is a “cleaner” codebase, but it’s arguable, especially when the code is neither often modified nor used. The value created here is not clear, and it would have been easier if the first refactoring never happened. In this specific situation, I recommend refactoring when you actively change part of the codebase for another reason (implementing a new feature for example).

Whether a problem is worthy to be solved is subjective. It also depends on the problem: if the solution is clear and straightforward, it might be useful to solve it, if the consequences of the solution are also clearly known and the risks are low. Unfortunately, these kinds of problems, in practice, are quite rare.

Types of Problems

I would define here two wide categories of problems: the problems with a (or multiple) clear solution (what the literature call “problem-solving”), and the problems without clear solution (it’s sometimes called “decision-making” instead of “problem-solving”).

In fact, if the problem you’re trying to solve has a clear, accepted answer, it’s very likely it has been solved already. It’s often the case for mechanical, technical problems. For example, let’s say that you need to order a list; you just have to search on the wild Internet how to do so in your programming language of choice, and you’re done! You can ask an “AI” too, or stack overflow, or whatever.

In my experience, most technical problems have one (or multiple) accepted solution. I won’t speak about these kinds of problems at length in this article, since they’re the easiest to solve.

When you’re in front of a problem which has no clear solution (even after doing some research), it’s where things get more complicated. I’d argue that most problems you’ll face, as a software developer, are of this category. Problems which are directly linked to the domain of the company you work with are often specific (because they depend on the domain), and complex.

For example, I’m working for a company providing a learning platform for medical students who want to become doctors, among other services. This context is changing because the real world is changing; medicine is no exception.

Recently, we had to create new data structures for the knowledge we provide; these data structures are directly linked to the domain (medicine) here. But what data structures to create? How can they adapt to the ever-changing environment? How to capture the data in the most meaningful way, with understandable naming for other developers?

Decisions had to be made, and when there are no clear solutions, you need to come up with a couple of hypothesizes. They won’t feel necessary like solutions , but rather decisions to take to move forward toward the desired outcome. It often ends up in compromises, especially if you’re working in a team where the members have different opinions .

Also, architectural decisions have often no clear solutions because they depend, again, on the changing context. How to be sure that an architectural decision is good today and in three months? How can we make the architecture flexible enough to adapt to the blurry future?

As developers, we deal with complex codebases, which are somewhat linked to the even more complex real world. It’s difficult to know beforehand the consequences of our decisions, as well as the benefits, the drawback, and the potential bugs we introduce.

Before jumping into the solution space however, we first need a good detour in the problem space.

Defining the Problem

Correctly stating the problem.

After determining that we indeed have some kind of problem, it’s tempting to try to find a solution directly. Be patient: it’s better to look at the problem more closely first.

If you don’t specify well the problem, you might not solve it entirely. It’s also possible that you end up solving the wrong problem, or the symptoms of a problem, that is, other minor problems created by a root problem. Often, the ideal scenario is to find the root problem, even if you don’t want to tackle it first. In any case, it’s always useful information.

For example, not long ago, our users didn’t find the content they were searching for, using our search functionality on our learning platform.

We could have directly solved the problem by asking the search team to adjust that for us, but this problem was only a symptom. It wasn’t the first time that we had to spend time and energy trying to communicate to the search team what we wanted to fix; the real root problem here was that we didn’t have any ownership of our search results.

The solution: we created a better API communicating with the search team, to be able to adjust ourselves the search results in a more flexible manner.

When looking at a problem, a good first step is to write it down. Don’t do it once; try to find different formulations for the same problem.

Writing is nice (I love it!), but other ways to represent ideas can be really useful too. You can try to draw what you understand from the problem: a drawing, a diagram, or even a picture can help you understand the problem.

From there, you can ask yourself: do you have enough information to take a decision? The answer will be mostly based on the experience of the problem solver, there is no magical formula to be sure that you can and will solve the problem.

You should also try to look at the problem from different angles, to really frame it correctly. The best way to do so is to solve problems as a team.

Solving Problems in a Team

Trying to describe and think about a problem is a great beginning, but it’s even better if you do it as a team. You can exchange experience, opinions, and it’s easier to look at a problem from multiple angles when multiple developers are involved.

First, make sure that everybody in the team is aware of the problem. Defining it altogether is the best. If you have a doubt that somebody is not on the same page, you can re-explain it using different words. It might bring more insights and ideas to the discussion.

Don’t assume that everybody understands the problem equally. Words are powerful, but they are also ambiguous; never hesitate to ask questions (even if they seem stupid at first), and encourage the team to do the same. If your colleagues see that you’re not afraid to ask, it will give them confidence to do the same.

The ambiguity can also build overtime, after the problem was discussed. That’s why it’s really important to document the whole process, for anybody to be able to look at it again and fix the possible creeping misconceptions. Don’t try to describe everything, but try to be specific enough. It’s a delicate balance, and you’ll get better at it with experience.

If you don’t like writing, I’d recommend you to try anyway: this is a powerful skill which will be useful in many areas of your life.

Regarding the team of problem solvers, diversity is important. Diversity of opinion, experience, background, you name it. The more diverse the opinions and ideas are, the more chances you’ll have to solve the problem satisfyingly (more on that later). If the members of the team have enough respect, humility, and know how to listen to their colleagues , you’re in the perfect environment to solve problems.

As developers, we’re dealing with moving systems, because they need to reflect the ever-changing business domain of the company you’re working with. These problems are unique, and even if similar problems might have been solved in the past, they’re never the exactly same. The differences can have an impact on the solution, sometimes insignificant (allowing you to re-apply the solution found previously), sometimes important enough to change the solution entirely.

Exploring the Solution Space

Now that we’ve defined the problem, thought about it with our team, tried to look at it from different angles, it’s time to try to find solutions, or at least to make a decision.

What is a good decision? The one which will bring you closer to your desired outcome. It sounds obvious, but there can be some ego involved in discussions, which will push us to try to be right even if it’s not the best solution in the current context. Our personal incentives can conflict with the company’s best interest; it’s always good to try to stay aware of that.

The solution should also be the simplest possible, while still moving forward to the desired outcome. It should also have an acceptable level of risk when we decide to apply the solution. In my experience, complicated solutions are the ones which come up first: don’t stop there. Take some time trying to find the best solution with your team.

For example, here’s what we do with my actual team:

• We define the problem altogether.
• We try to think about different hypothesizes. Not only one, but a couple of them.
• We write the benefits and drawbacks of each hypothesis (which can lead to more ideas, and possibly more hypothesizes).
• We commit to a hypothesis, which then needs to be implemented.

What I meant by “hypothesis” here is a solution which might work; but only the implementation of the hypothesis can be considered as a solution. Before the implementation, it’s just an informed guess. Many things can go wrong during an implementation.

This process looks simple, but when you have multiple developers involved, it’s not. Again, if each member of the team have good soft skills and some experience, it can be an enjoyable and rewarding process. But you need a good team for it to work efficiently (that’s why it’s so important to ask the good questions when joining a company). It’s even better if the members of the team are used to swim in uncertainty, and take it as a challenge more than a chore.

The process described above is just an example; in practice it’s often more chaotic. For example, even when a decision is made, your brain might still continue to process the problem passively. If you find some flaws in the hypothesis you’ve committed to, congratulations! You have now a brand-new problem.

I can’t emphasize it enough: try to be as detached as possible from your ideas, opinions, and preferred hypothesizes. The goal is not for you to be right and feel good, but for your company to move in the good direction. It’s hard, but with practice it gets easier.

I also want to underline the importance of finding both benefits and drawbacks for the different hypothesizes you (and your team) came up with.

To find good solutions, we might also need to reduce the uncertainty around their possible consequences. Doing some external research can help, like gathering data around the problem and the possible hypothesizes. In the best case scenario, if you can find enough data, and if you feel confident that you can move forward with a hypothesis, that’s already a great victory.

If you don’t have enough external information to reduce the uncertainty to a level you feel comfortable with, look at your past experience. Try to find problems similar to the one your deal with in the present, and try to think about the solutions applied at the time, to see if they could also be applied in your current case. But be careful with this approach: complex problems are context-sensitive, and the context you were in the past will never be exactly the same as the present and future contexts.

For example, I recently changed the way we display search results in our system, because we had some data indicating that some users had difficulties to find what they really wanted to find. The problem: users have difficulties to find the good information; it’s a recurrent problem which might never be 100% solved. That said, thanks to the data gathered, we found an easy way to improve the situation.

The data was very clear and specific, but it’s not always the case. More often than not, your data won’t really prove anything. It might only show correlations without clear causality. It will be even more true if you begin by gathering data without defining first the problem you try to solve. You can find problems looking at some data, that’s true, but it needs care and deep understanding of what you’re doing; looking at data when you know exactly what you want to solve works better.

Using this kind of process, the hypothesis is often some sort of compromise. That’s fine; committing to a hypothesis is not the end of the process, and there will be other occasions to revisit and refine the solution.

If you don’t feel comfortable with the level of uncertainty of the problem (or the risk involved by applying your hypothesis), you need to dig more. Writing a prototype can be useful for example, if you hesitate between two or more approaches. If your prototype is convincing enough, it can also be useful to gather feedback from your users, even if the ones testing your hypothesis will always be more invested if they test a real-life functionality, instead of a prototype which might use dummy data, or be in a context which is too remote from the “real” context.

In my opinion, prototypes are not always useful for complex problems, because a prototype only test a new feature at time T, but doesn’t allow you to see if the solution stay flexible enough overtime. That’s often a big concern: how will the solution evolve?

But prototyping can still help gather information and reduce the uncertainty of the problem, even if the prototype doesn’t really give you the solution on a silver platter. It’s also great for A/B testing, when you’re in the (likely) case when you have not much information about the real needs of your users. You could ask them of course, but nothing guarantee that they know themselves what these needs are.

If you don’t find any satisfying hypothesis to your problem, you might also challenge the desired outcome. Maybe a similar, simplest hypothesis, with slightly different outcomes, could work better? If it makes things easier, faster, and less complex, it could be the best solution. Don’t hesitate to challenge your stakeholders directly on the desired outcomes.

Deferring the Problem

In some cases, you might be hesitant to try to solve a problem if there is still too much uncertainty around it. In that case, it might be best to defer solving the problem altogether.

Deferring the problem means that you don’t solve it now ; you keep things as they are, until you get more information to reduce the uncertainty enough.

We had a problem in the company I worked with some time ago: we have dosages which can be discovered in articles, but users didn’t really find them, and nobody really knew why. Because of this lack of information, the problem was not tackled right away, but differed. From there, data have been collected overtime, allowing us to understand the scope of the problem better.

Don’t forget that deferring a problem is already taking a decision. It might be the less disruptive decision for the application and its codebase, but it’s s decision nonetheless, and it can have consequences. Seeing a differed problem as a decision will push you to think about the possible consequences of your inaction, and you’ll look at it as a partial “solution”, with some uncertainty and risk associated to it.

In my experience, deferring the problem works well only when you try to actively seek more data to solve it later. It can be some monitoring to see how the problem evolves, or some data taken from users’ actions. Sometimes, simply waiting can also give you important information about the nature of the problem.

What you shouldn’t do is try to forget the problem. It might come back in force to haunt your sleepless nightmares later. Avoiding a problem is not deferring it.

Here’s another example: we began recently to build some CMS tooling for medical editors, for them to write and edit content on our learning platform. We had one GraphQL API endpoint at the beginning, providing data to two different part of the application:

• Our CMS for medical editors.
• Our learning platform for medical students.

We knew that using one single GraphQL endpoint for these two types of users could cause some problems.

But we didn’t do anything about it, mostly because we didn’t see any real, concrete problem, at least at first. When a minor symptom, related to this unique endpoint, popped up, we spoke about it, and we still chose not to do anything. We preferred deferring the problem once more, to try to solve the real problem (one API for two different kinds of applications) later.

Finally, when we had enough symptoms and some frustration, we decided to split our graphQL API in two different endpoints. It was the best moment to do so: we had enough information to come up with a good decision, we applied it, and we stayed vigilant, to see how our applied hypothesis would evolve.

Moving fast and breaking things is not always the best solution. In some situations, waiting a bit and see how things evolve can allow you to solve your problems in a more effective way. But, as always, it depends on the problem, its context, and so on.

Reading this article, you might have wondered: how much information is enough to be comfortable enough to apply a solution? Well, again, your experience will be the best judge here. You’ll also need to consider carefully risks, benefits, and drawbacks. It doesn’t mean that you need to chicken out if you don’t have 100% certainty about a problem and some hypothesizes; being a software developer implies to have some courage and accept that mistakes will be made. It’s not an easy task, and there is no general process to follow in any possible case.

In short: use your brain. Even if you’re totally wrong, you’ll have the opportunity to fix the bad decisions you’ve made before the implementation, during the implementation, and even after it. We don’t code in stone.

The Implementation: The Value of Iteration

You’ve gathered with your team, tried to define the problem, found multiple hypothesizes, and agreed to try one of them. Great! Problem solved.

Not so fast! We still need to apply the hypothesis, and hope that it will become a good solution to the problem. Doing so, you’ll gather more information along the way, which might change your perspective on the problem, on your hypothesizes, and can even create some baby problems on its own.

It’s where the agile methodology is useful: since we’ll never have 100% certainty regarding a problem and its possible solution, we’ll learn more about both while implementing the hypothesis. That’s why it’s so valuable to iterate on the implementation: it gives you more information to possibly adjust your code, or even the problem, or even switching hypothesizes altogether. Who knows? A solution which is not implemented is just a guess.

If the hypothesis applied is not the ones you would have personally preferred (compromising, or even giving up on your preferred solution is common in a team), only applying it will tell you if you’re right or wrong; that is, if the hypothesis can become a solution solving the problem, at least in the present context.

If you’re worried about how a specific solution will evolve overtime, it’s more complicated, because an implementation won’t give you the information you seek. Still, implementing a hypothesis can be a great source of learning (the most valuable to me is when I’m wrong, because I learn even more). If you think that your hypothesis can have better outcome at time T, you might also try to implement it and compare it. Again, it’s where prototyping is useful.

When applying the solution, you need to look at the details of the implementation, as well as the big picture, to judge if the solution you’re creating is appropriate (leading to the desired outcome). This is a difficult exercise. In general, a developer should be able to reason on different levels of abstraction, more or less at the same time. Again, if you’re aware of it, your experience will help you here, and you can also push yourself to think of all the possible risks and consequences at different levels.

If you work in a team, try to participate (at least a bit) into the implementation of the solution. It’s not good to create silos in teams (that is, only a couple of members have some information others don’t have).

You can go as far as looking at other projects, and ask yourselves these questions:

• Did we had similar problems on these other projects? How did we solve them?
• What was the context of these projects? Is it similar to our current context?
• What did we learn from these other problems, and their implementation? Is the implementation similar to what we’re doing now?

In any case, I would definitely recommend you to write a development journal. I write mine for years, and it has been valuable in many cases. I basically write in there:

• The interesting problems I had.
• The decisions made.
• How the implementation of the solution evolved overtime.
• The possible mistakes we made along the way.

It’s a great resource when you have a problem and you want to look at your past experience.

To evaluate your decisions overtime, nothing will beat a good monitoring process: logs, tests, and so on. It’s what the book Building Evolutionary Architecture call “fitness functions” for example, some monitoring allowing you to measure how healthy your architecture stays overtime. It doesn’t have to stop to the architecture; you can think about different monitoring system to see how something evolve, especially if the solution has still a lot of uncertainty regarding its benefits, drawbacks, and risks.

You can also do that retrospectively: looking at how the code complexity evolve overtime using Git for example.

Retrospective on the Process

We defined the problem, implemented a solution iteratively, and now the problem is gone. That’s it! We made it! Are we done now?

Decisions are sometimes not optimal, and implementing a solution successfully doesn’t mean that there wasn’t a better (simpler) one to begin with. That’s why it can be beneficial to look back and understand what went right, and what went wrong. For example, we can ask ourselves these questions:

• Looking at what we learned during the whole process, is there a potentially better hypothesis to solve the problem in a simpler, more robust way?
• What are the benefits and drawbacks we missed when speaking about the different hypothesizes, but we discovered during the implementation? Why we didn’t think about them beforehand?
• What other problems did we encounter during the implementation? Did we solve them? Did we differ some? What should be the next steps regarding these new problems?
• What kind of monitoring did we put in place to make sure that the solution won’t have undesired outcomes overtime? Can we learn something with this data?

Reflecting on past solutions is a difficult thing to do. There is no way to logically assess that the decision taken was better than others, since we didn’t implement the other hypothesizes, and we didn’t look at them overtime to appreciate their consequences. But you can still look at the implementation of the solution overtime, and write in your developer journal each time there is a bug which seems directly related to the solution. Would the bugs be the same if another solution would had been applied?

Bugs are often not an option; they will pop up, eventually. Nonetheless, it’s important to make sure that you can fix them in a reasonable amount of time, and that you don’t see them creeping back in the codebase after being solved. Some metrics, from the DevOps movement (like MTTR for example) can help here. Sometimes, bugs will show you a better, more refined solution to the original problem; after all, bugs can also give you some useful information. They are also the most direct result of the implementation of your solution.

If you want to know more about measuring complexity (which can be also used to measure complexity overtime after applying a solution), I wrote a couple of articles on the subject .

Humility in Problem-Solving

It’s time to do a little summary. What did we see in this article?

• We need to ensure that the problem we found is really a problem we need to solve. Is there any value to solve the problem? Is it even a problem?
• Try to determine what kind of problem you have: a problem which can have multiple, specific, known answers (like a technical problem), or a problem which depends on the real-life context, without known solutions?
• Defining the problem is important. Try to define it using different words. Write these definitions down. Does everybody in your team understand the problem equally?
• It’s time to explore the solution space. Draft a couple of hypothesizes, their benefits, drawbacks, and risks. You can also do some prototyping if you think it would give you more information to take the best decision.
• Do you have enough information to implement a hypothesis, becoming effectively a solution? If it’s not the case, it might be better to keep the status quo and try to solve the problem later, when you’ll have more information. But don’t forget the problem!
• If you decide to implement a solution, do it step by step, especially if you’re unsure about the consequences of your decisions. Implement an independent part of the hypothesis, look at the consequences, adjust if necessary, and re-iterate.
• When the solution is implemented, it’s time to reflect on the whole process: did we solve the problem? What other problems did we encounter? Maybe another solution would have been better? Why?

As I was writing above, most problems you’ll encounter will be complex ones, embedded into a changing environment with different moving parts. As a result, it’s difficult to train to solve problems in a vacuum; the only good training I know is solving real life problems. That’s why your experience is so important.

Experience build your intuition, which in turn increase your expertise.

You’ll never have 100% certainty that a solution will bring you the desired outcome, especially if you are in front of a complex problem with a blurry context. If you are absolutely convinced that you have the good solution without even beginning to implement it, I’d advise you to stay humber in front of the Gods of Complexity, or they will show you how little you know.

• How to solve it
• Hammock Driven Development
• When Deferring Decisions Leads to Better Codebases
• Lean Development - deferring decision

Tutorial Playlist

Programming tutorial, your guide to the best backend languages for 2024, an ultimate guide that helps you to start learn coding 2024, what is backend development: the ultimate guide for beginners, all you need to know for choosing the first programming language to learn, here’s all you need to know about coding, decoding, and reasoning with examples, understanding what is xml: the best guide to xml and its concepts., an ultimate guide to learn the importance of low-code and no-code development, top frontend languages that you should know about, top 75+ frontend developer interview questions and answers, the ultimate guide to learn typescript generics, the most comprehensive guide for beginners to know ‘what is typescript’.

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Most commonly asked logical reasoning questions in an aptitude test, everything you need to know about advanced typescript concepts, an absolute guide to build c hello world program, a one-stop solution guide to learn how to create a game in unity, what is nat significance of nat for translating ip addresses in the network model, data science vs software engineering: key differences, a real-time chat application typescript project using node.js as a server, what is raspberry pi here’s the best guide to get started, what is arduino here’s the best beginners guide to get started, arduino vs. raspberry pi: which is the better board, the perfect guide for all you need to learn about mean stack, software developer resume: a comprehensive guide, here’s everything all you need to know about the programming roadmap, an ultimate guide that helps you to develop and improve problem solving in programming, the top 10 awesome arduino projects of all time, pyspark rdd: everything you need to know about pyspark rdd, wipro interview questions and answers that you should know before going for an interview, how to use typescript with nodejs: the ultimate guide, what is rust programming language why is it so popular, software terminologies, an ultimate guide that helps you to develop and improve problem solving in programming.

Lesson 27 of 33 By Hemant Deshpande

Table of Contents

Coding and Programming skills hold a significant and critical role in implementing and developing various technologies and software. They add more value to the future and development. These programming and coding skills are essential for every person to improve problem solving skills. So, we brought you this article to help you learn and know the importance of these skills in the future. 

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Topics covered in this problem solving in programming article are:

• What is Problem Solving in Programming? 
• Problem Solving skills in Programming
• How does it impact your career ?
• Steps involved in Problem Solving
• Steps to improve Problem Solving in programming

What is Problem Solving in Programming?

Computers are used to solve various problems in day-to-day life. Problem Solving is an essential skill that helps to solve problems in programming. There are specific steps to be carried out to solve problems in computer programming, and the success depends on how correctly and precisely we define a problem. This involves designing, identifying and implementing problems using certain steps to develop a computer.

When we know what exactly problem solving in programming is, let us learn how it impacts your career growth.

How Does It Impact Your Career?

Many companies look for candidates with excellent problem solving skills. These skills help people manage the work and make candidates put more effort into the work, which results in finding solutions for complex problems in unexpected situations. These skills also help to identify quick solutions when they arise and are identified. 

People with great problem solving skills also possess more thinking and analytical skills, which makes them much more successful and confident in their career and able to work in any kind of environment. 

The above section gives you an idea of how problem solving in programming impacts your career and growth. Now, let's understand what problem solving skills mean.

Problem Solving Skills in Programming

Solving a question that is related to computers is more complicated than finding the solutions for other questions. It requires excellent knowledge and much thinking power. Problem solving in programming skills is much needed for a person and holds a major advantage. For every question, there are specific steps to be followed to get a perfect solution. By using those steps, it is possible to find a solution quickly.

The above section is covered with an explanation of problem solving in programming skills. Now let's learn some steps involved in problem solving.

Steps Involved in Problem Solving

Before being ready to solve a problem, there are some steps and procedures to be followed to find the solution. Let's have a look at them in this problem solving in programming article.

Basically, they are divided into four categories:

• Analysing the problem
• Developing the algorithm
• Testing and debugging

Analysing the Problem

Every problem has a perfect solution; before we are ready to solve a problem, we must look over the question and understand it. When we know the question, it is easy to find the solution for it. If we are not ready with what we have to solve, then we end up with the question and cannot find the answer as expected. By analysing it, we can figure out the outputs and inputs to be carried out. Thus, when we analyse and are ready with the list, it is easy and helps us find the solution easily. 

Developing the Algorithm

It is required to decide a solution before writing a program. The procedure of representing the solution  in a natural language called an algorithm. We must design, develop and decide the final approach after a number of trials and errors, before actually writing the final code on an algorithm before we write the code. It captures and refines all the aspects of the desired solution.

Once we finalise the algorithm, we must convert the decided algorithm into a code or program using a dedicated programming language that is understandable by the computer to find a desired solution. In this stage, a wide variety of programming languages are used to convert the algorithm into code. 

Testing and Debugging

The designed and developed program undergoes several rigorous tests based on various real-time parameters and the program undergoes various levels of simulations. It must meet the user's requirements, which have to respond with the required time. It should generate all expected outputs to all the possible inputs. The program should also undergo bug fixing and all possible exception handling. If it fails to show the possible results, it should be checked for logical errors.

Industries follow some testing methods like system testing, component testing and acceptance testing while developing complex applications. The errors identified while testing are debugged or rectified and tested again until all errors are removed from the program.

The steps mentioned above are involved in problem solving in programming. Now let's see some more detailed information about the steps to improve problem solving in programming.

Steps to Improve Problem Solving in Programming

Right mindset.

The way to approach problems is the key to improving the skills. To find a solution, a positive mindset helps to solve problems quickly. If you think something is impossible, then it is hard to achieve. When you feel free and focus with a positive attitude, even complex problems will have a perfect solution.

Making Right Decisions

When we need to solve a problem, we must be clear with the solution. The perfect solution helps to get success in a shorter period. Making the right decisions in the right situation helps to find the perfect solution quickly and efficiently. These skills also help to get more command over the subject.

Keeping Ideas on Track

Ideas always help much in improving the skills; they also help to gain more knowledge and more command over things. In problem solving situations, these ideas help much and help to develop more skills. Give opportunities for the mind and keep on noting the ideas.

Learning from Feedbacks

A crucial part of learning is from the feedback. Mistakes help you to gain more knowledge and have much growth. When you have a solution for a problem, go for the feedback from the experienced or the professionals. It helps you get success within a shorter period and enables you to find other solutions easily.

Asking Questions

Questions are an incredible part of life. While searching for solutions, there are a lot of questions that arise in our minds. Once you know the question correctly, then you are able to find answers quickly. In coding or programming, we must have a clear idea about the problem. Then, you can find the perfect solution for it. Raising questions can help to understand the problem.

These are a few reasons and tips to improve problem solving in programming skills. Now let's see some major benefits in this article.

• Problem solving in programming skills helps to gain more knowledge over coding and programming, which is a major benefit.
• These problem solving skills also help to develop more skills in a person and build a promising career.
• These skills also help to find the solutions for critical and complex problems in a perfect way.
• Learning and developing problem solving in programming helps in building a good foundation.
• Most of the companies are looking for people with good problem solving skills, and these play an important role when it comes to job opportunities 

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Problem solving in programming skills is important in this modern world; these skills build a great career and hold a great advantage. This article on problem solving in programming provides you with an idea of how it plays a massive role in the present world. In this problem solving in programming article, the skills and the ways to improve more command on problem solving in programming are mentioned and explained in a proper way.

If you are looking to advance in your career. Simplilearn provides training and certification courses on various programming languages - Python , Java , Javascript , and many more. Check out our Full Stack Developer - MERN Stack course that will help you excel in your career.

If you have any questions for us on the problem solving in programming article. Do let us know in the comments section below; we have our experts answer it right away.

Find our Full Stack Developer - MERN Stack Online Bootcamp in top cities:

About the Author

Hemant Deshpande, PMP has more than 17 years of experience working for various global MNC's. He has more than 10 years of experience in managing large transformation programs for Fortune 500 clients across verticals such as Banking, Finance, Insurance, Healthcare, Telecom and others. During his career he has worked across the geographies - North America, Europe, Middle East, and Asia Pacific. Hemant is an internationally Certified Executive Coach (CCA/ICF Approved) working with corporate leaders. He also provides Management Consulting and Training services. He is passionate about writing and regularly blogs and writes content for top websites. His motto in life - Making a positive difference.

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• PMP, PMI, PMBOK, CAPM, PgMP, PfMP, ACP, PBA, RMP, SP, and OPM3 are registered marks of the Project Management Institute, Inc.

Problem-Solving Skills for Software Developers: Why & How to Improve

Problem-solving skills go hand-in-hand with software development. Learn some great problem-solving techniques and tips for improvement here!

Software developer jobs today require that you possess excellent problem-solving skills , and for good reason. Unfortunately, there seems to be a sort of talent gap when it comes to this one skill required of all software developers.

Troubleshooting and problem resolution are both informally and formally taught, but you mostly find that software developers have to learn problem-solving skills on their own. This is true for self-taught developers , obviously, but also even for those with software engineering degrees or who’ve graduated from coding boot camps.

This is why it’s necessary to acquaint yourself with the problem-solving process, whether you are a newbie or an experienced developer. In this article, we’ll explore everything you need to know about problem-solving so you can 10x your software development career.

What are Problem-Solving Skills?

As a developer, what do we mean by problem-solving? Let’s attempt a simple definition.

In software development, problem-solving is the process of using theories and research to find solutions to a problem domain, while testing different ideas and applying best practices to achieve a desired result. Problem-solving also has to do with utilizing creativity and logical thought processes to identify problems and resolve them with software.

Becoming a great software developer hinges more on learning algorithms than programming languages or frameworks . And algorithms are simply step-by-step instructions to solve a given problem.

Read More : How to Build a Software Engineer Portfolio (With Examples & Tips)

Why are impeccable problem-solving skills crucial?

Making good use of a computer language can be likened to being a skilled writer. An effective writer must know how to construct sentences and use grammar appropriately. There’s more to writing than just knowing all the words in the dictionary, and that’s how it works for developers, too.

You have different tasks to work on as a software developer, including perhaps designing, coding, and troubleshooting. Much of your time will be spent on identifying problems, spotting and correcting bugs, and making sense of codebases from before you started working there. Being ingenious at problem-solving is essential in creating incredible solutions to issues that arise throughout software development.

To demonstrate ingenuity, let’s consider Google’s autocomplete tool as an example.

The autocomplete tool is built to suggest related terms in the search bar as you type. The idea behind the tool is to reduce more than 200 years of time spent typing daily and to help users save time by up to 25% while typing.

Here’s what had to be done:

• To activate real-time completion of suggestions, the UI experience and JavaScript had to be implemented.
• Next, since users could type just about anything, the autocomplete suggestions had to be compiled into a sensible list dependent on user input.
• Then, Google had to create a back-end sustainability system for this function. Doing this meant massively increasing its infrastructure to accommodate all forms of data query and HTTP requests.
• Finally, the user interface had to be refined by software engineers in order to make sure that every user enjoyed a worthwhile experience. So they employed Google Trends to power the auto-completion tool while using algorithms to take out explicit or offensive predictions in line with Google’s auto-completion policy.

This is just one of Google’s innumerable problem-solving examples, but it’s clear to see that solving problems involves more than just telling a computer to do stuff. It’s about your ability to come up with parameters rightly tailored to target users so they can meet their goals.

So why must developers focus on problem-solving at work?

Software developers work with a wide range of people and departments, and it’s common to discover that some clients and teams find it difficult to define what they want. As a problem solver, it’s up to you to help them identify their needs and communicate their thoughts in an effective way.

Of course, you’ll need time and practice to develop your problem resolution ability. That’s because it’s less about solving problems faster but more about coming up with the best solution . And then you’ll need to deploy that solution.

Read More : Common Interview Questions for Software Developer Jobs (Non-Technical)

Types of problem-solving skills

Now let’s talk about four types of problem-solving skills for developers:

1.  Parallel thinking

As a software developer, parallel thinking is a crucial skill necessary to perform optimally. This makes it possible for you to carry out two tasks that complement each other at the same time (like an optimized form of multitasking skills). Being able to reorder tasks to boost parallel execution can help to improve your output and save valuable time .

2. Dissecting broad and/or complex goals

When it comes to building software, you will need to effectively outline the steps and tasks necessary to achieve your goal. Developers must learn to break large and complex tasks into smaller assignments because this is an important skill that will help you create results with precision.

3. Reimplementing existing solutions

You don’t always need to reinvent the wheel. Part of being an effective software developer comes with being able to use already existing tools before even thinking of creating new solutions. Developing problem-solving skills is very much connected to finding solutions that already exist and reusing them.

4. Abstraction

Keep in mind that goals tend to evolve. So if your client comes up with new ideas, that will mean changing your design goals and reordering your tasks. A good programmer must learn to create solutions in such a way that does not require a complete redesign from scratch.

You also have to become adept at abstracting problems so that your solutions can get them resolved so long as they aren’t entirely different from the original issue. You don’t necessarily have to abstract every aspect to avoid more complications being created. This calls for balance by abstracting only where necessary without making narrow decisions.

Read More : Learn 8 Great Benefits of Working From Home

4 Important Tips & Strategies for Improving Problem-Solving Skills

To keep your problem-solving skills and techniques from growing weaker over time, you need to exercise them non-stop. As they say: practice makes perfect!

To train the problem-solving side of your brain, these four tips and strategies can help you improve your abilities:

1. Make problem-solving a part of your life

Never restrict yourself to working on problems only during work hours. Don’t make it a chore, but, instead, do things that make problem-solving look fun. The game of chess, solving puzzles, and playing video games that compel you to think critically will help strengthen your problem-solving skills, and you can tell your significant other you are advancing your career! 🙂

When you come to a complex problem in your life, whether it’s budgeting for a home or renovating the downstairs bathroom, approach it both creatively and critically. Ask yourself: What would a great software engineer do in this situation?

2. Use different platforms to solve problems

Proffer solutions to a set of problems without restricting yourself to one platform. Using different platforms and tools regularly helps make sure you become flexible as a problem-solver. And it makes sense, because there really is no universal solution for the different problems that pop up in your line of work. Trying out different platforms to solve different problems helps you to keep an open mind and enables you to test out different techniques when looking to find solutions.

Read More : 12 Common Mistakes Keeping You From Landing Your First Developer Job

3. Be open to assistance from external sources

Part of being a good software developer comes with being able to ask for help and also accept all forms of feedback. You might need a different opinion or a new set of eyes to help find the most fitting solution to some problems. It makes sense to view building problem-solving skills as more of a team effort rather than a personal journey.

Have an open mind and heart to function not only as an individual but also as a collective. It’s a utopian working environment where everyone supports each other to become better versions of themselves. So if you come across an issue that keeps you stuck, get help! You may find someone who has a more refined framework or method you never knew existed or would have thought of using. You could then learn from them and add their solution to your toolkit.

Get feedback often, as well. This could be the catalyst to making improvements to your processes and evolving them into something truly refined.

4. Tackle new problems using lessons from past solutions

As you practice and finesse your ability to identify problems and find solutions, you’ll begin to notice patterns. It’s more like developing your toolbox armed with a wide range of solutions that have proved useful in the past. So when problems emerge, you will notice how easy it is to take some of those old solutions and apply them to the new problem.

The more you attempt to apply creativity in solving problems, the more you grow your skills. In the long run, that will help you find the right solutions faster and apply them to a wide range of problems more naturally. It’s all about improving the effectiveness and efficiency with which you tackle new problems while applying only the best possible solutions.

Read More : How to Stay Motivated at Work

3 Complementary Skills to Improve to Become a Good Problem Solver

Developing software is mostly about problem-solving at the very core before even writing your first lines of code. You have to identify problems that can be solved using software. Then you have to go on to understand how people try to solve such problems in real life.

It’s up to you to come up with a framework that allows you to take both the problem and the solution and convert them into computer code. And you have to do this in such a way that makes the software even more efficient and effective than a human.

While going through this process, developers also have to handle other problems such as deadline deliveries, checking for bugs and fixing them, and collaborate across teams. So, supporting skills must not be overlooked.

Software developers must build interpersonal skills and collaboration skills . Being able to empathize, accept feedback, handle criticism, listen intently, and show respect for others are all important characteristics and abilities necessary for teamwork, and, thus, necessary for solving problems on the job.

Read More : 5 Ways to Stand Out & Get Noticed in Your Current Development Job

Communication

No one is an island, and that’s true when you consider how software engineers work. Building software requires keeping up with clients and teammates and other departments. You can’t afford to be a Lone Ranger, at least not 100% of the time, and that’s why employers always look for good communication skills.

Being a good software developer also involves how well you can break down very complex concepts to laypeople. You want to be the kind of person who fixes a problem and is able to explain how you were able to do it. It’s all about your ability to be clear and articulate about every aspect of your work. And you want to be able to communicate not just verbally but also in written form.

To build your communication skills as a developer, you can learn from more experienced people and observe how they interact with their clients. And, don’t forget, with more and more companies becoming global enterprises and going remote, it’s important to brush up on your intercultural communication skills , as well.

Logical thinking

The difference between elite software developers and average ones is often said to be logical thinking. The ability to process thoughts logically is important, because you’ll often spend most of your time finding and fixing bugs rather than writing code.

Problems can show up from just about anywhere, even from what seems to be the most insignificant errors. So, your ability to detect software issues and solve these problems using deductive thought processes is a vital ingredient to your success as a software developer.

Read More : Questions to Ask at Interviews for Software Engineering Jobs

Problem-Solving Stages & Practices

There are countless problem-solving processes and various schools of thought regarding the best way to approach problems whenever they arise. To solve that problem, we’ve pooled some of these frameworks together to come up with a comprehensive approach to problem-solving.

Step 1 – Define the problem

You have to first start with problem identification. Knowing what you are dealing with is important, because you don’t want to risk spending valuable time applying wrong solutions. Avoid making automatic assumptions. Even when the symptoms look familiar, you want to investigate properly because such signs could be pointing to something else entirely.

Problems in software development come in different sizes and scopes. You could be having trouble getting some aspects of the product to respond in the desired way. Or maybe you’re having issues trying to decipher a codebase section where you can no longer communicate with the original developers. Sometimes, the problem could come in the form of an unfamiliar error message and you’re at loss.

Once you’re able to define the problem, make sure to document it.

Step 2 – Analyze the problem

Now it’s time to carry out problem analysis . Before deciding what problem resolution methods to adopt, it’s necessary to find out all there is to the issue, which builds on our first step. This will make it easier to come up with ideas and solutions later on.

Problem analysis isn’t always a walk in the park. There are times when the problem involves a very small mistake such as failing to import a package correctly or a small syntax error. Other times, however, it could be such a huge error, like the entire program acting differently than what you want. There might be no alarms or blinking red lights to tell you what the exact problem is.

If you encounter such situations, you can find answers by articulating the problem. Document what you intend to do, what you’ve done, the original intention for the program, and where you currently are. Communication comes in handy here, of course, not just in your documentation, but also in how you relay it to your teammates.

Read More : Got a Busy Developer Schedule? Here’s How to Keep Learning & Make Time

Step 3 – Brainstorm

This step has to do with generating ideas, and you can benefit from discussing the problem with a team and then coming up with ways to get it fixed. Keep in mind that problem-solving at work involves interacting with a diverse group of people where the individuals have unique skill sets and experiences.

Many developers tend to neglect the previous steps and rush straight into brainstorming. That’s definitely not a good way to go about problem-solving. The idea is not to skip the important steps in the process.

Once you get to the point where ideas need to be generated, do not discard any, because this step relies on a wide range of ideas. Only after gathering as many perspectives as possible should you then begin reviewing and narrowing down to the best possible solution.

Step 4 – Make a decision

At this point, all viable solutions have to be analyzed before selecting the most appropriate one to implement. Picking the best possible solution depends on its ability to meet certain criteria. It must be suitable, feasible, and then acceptable.

What it means is that the solution must be able to get the problem solved. It should also be easy to see how such a solution fits into the equation. And then every member of the team involved in the brainstorming process has to unanimously accept the solution.

Read More : How to Network as a Software Engineer

Step 5 – Implement

After identifying and choosing the solution, the next logical step is to plan out the implementation process and then execute it. Coming up with a detailed plan is crucial if the solution is to be a success.

Now this plan must detail all the necessary steps required to implement the solution. It will also explain the length of time and stages of work required. Once all of that is put in place, you can then move forward with the execution. The idea is not just to execute a solution but to do it the right way.

Implementation using automated tests can help to keep unexpected issues from arising in the future. Some other problem-solving practices or approaches begin the process with this step. So, whenever any changes are made to the project, tests asserting that the changes will perform as required will be written first before the changes are then made.

Step 6 – Evaluate

No problem-solving process can be deemed comprehensive enough if there is no room for evaluation. Whatever the solution may be, it has to undergo strict evaluation in order to see how it performs. That will also help determine whether the problem still exists and the extent to which such an issue keeps recurring.

In the event that the problem persists despite the implementation of a detailed plan, then the developer and team may even have to restart the problem-solving process. However discouraging that may sound, at least you’ll have caught it early enough. And, this also proves the process worked.

Read More : How to Become a Software Engineer: Education, Steps & Tips for Success

Final Thoughts

Developing problem-solving skills is quite necessary for software developers. To be a successful problem solver, you will need lots of years down the line to practice what you study.

Always remember that you are a problem solver first before anything else. There is more to building software than just understanding the tech behind it and writing lines of code. It’s all about improving your ability to identify problems and find solutions, and that will need lots of experience on your part.

Never shy away from problems, but learn to think critically and logically in any situation. By applying the six-step strategy for problem-solving at work discussed in this piece, you will be more equipped to come up with the most effective and efficient solutions.

We hope you enjoyed reading our guide on how to solve a problem as a software developer and ways to improve skills as a problem solver! If you have any questions, feedback, or other great problem-solving techniques or methods, let us know in the comments below 🙂

The Arc team publishes insightful articles and thought leadership pieces related to software engineering careers and remote work. From helping entry-level developers land their first junior role to assisting remote workers struggling with working from home to guiding mid-level programmers as they seek a leadership position, Arc covers it all and more!

Further reading

Here Are 43 of the Best Online Developer Communities to Join in 2024

Ready to Take On a Senior Role or Leadership Position as a Developer?

Key Analytical Skills for Developers (& How to Continually Improve Them)

Here’s When You Can TRULY Call Yourself a “Senior” Software Developer

Time Management Skills for Developers: Best Tips, Tools, and Strategies

Software Engineer Degree: Pros, Cons & Alternatives

UNIT 1: How to Think Like an Engineer

Learning objectives.

• Explain what we mean by “Computational Thinking”.
• Describe the problem being solved in a computational algorithm.
• Explain the process for generating computational algorithms.
• Generate and test algorithms to solve computational problems.
• Evaluate computational algorithms for exactness, correctness, termination, generalizability and understandability.
• Explain the role of programming in the field of Informatics.

Introduction

The goal of this book is to teach you to solve computational problems and to think like an engineer. Computational problems are problems that can be solved by the use of computations (a computation is what you do when you calculate something). Engineers are people who solve problems – they invent, design, analyze, build and test “things” to fulfill objectives and requirements. The single most important skill for you to learn is problem solving. Problem solving means the ability to formulate problems, think creatively about solutions, and express a solution clearly and accurately. As it turns out, the process of learning to program is an excellent opportunity to practice problem-solving skills.

This book strives to prepare you to write well-designed computer programs that solve interesting problems involving data.

Computational Thinking

Figure 1: “The seven components to computational thinking”(www.ignitemyfutureinschool.org/about)

Computational Thinking is the thought processes involved in understanding a problem and expressing its solution in a way that a computer can effectively carry out. Computational thinking involves solving problems, designing systems, and understanding human behavior (e.g. what the user needs or wants) – thinking like an engineer. Computational thinking is a fundamental skill for everyone, not just for programmers because computational thinking is what comes before any computing technology. [1]

Computer science is the study of computation — what can be computed and how to compute it whereas computational thinking is:

Conceptualizing , not programming. Computer science is not only computer programming. Thinking like a computer scientist means more than being able to program a computer. It requires thinking at multiple levels of abstraction;

Fundamental , not rote skill. A fundamental skill is something every human being must know to function in modern society. Rote means a mechanical routine;

A way that humans, not computers, think . Computational thinking is a way humans solve problems; it is not trying to get humans to think like computers. Computers are dull and boring; humans are clever and imaginative. We humans make computers exciting. Equipped with computing devices, we use our cleverness to tackle problems we would not dare take on before the age of computing and build systems with functionality limited only by our imaginations;

Complements and combines mathematical and engineering thinking . Computer science inherently draws on mathematical thinking, given that, like all sciences, its formal foundations rest on mathematics. Computer science inherently draws on engineering thinking, given that we build systems that interact with the real world;

Ideas , not artifacts. It’s not just the software and hardware artifacts we produce that will be physically present everywhere and touch our lives all the time, it will be the computational concepts we use to approach and solve problems, manage our daily lives, and communicate and interact with other people;

For everyone, everywhere . Computational thinking will be a reality when it is so integral to human endeavors it disappears as an explicit philosophy. [2]

Figure 2 “Are you happy?” by Typcut http://www.typcut.com/headup/are-you-happy

An algorithm specifies a series of steps that perform a particular computation or task. Throughout this book we’ll examine a number of different algorithms to solve a variety of computational problems.

Algorithms resemble recipes. Recipes tell you how to accomplish a task by performing a number of steps. For example, to bake a cake the steps are: preheat the oven; mix flour, sugar, and eggs thoroughly; pour into a baking pan; set the timer and bake until done.

However, “algorithm” is a technical term with a more specific meaning than “recipe”, and calling something an algorithm means that the following properties are all true:

• An algorithm is an unambiguous description that makes clear what has to be implemented in order to solve the problem. In a recipe, a step such as “Bake until done” is ambiguous because it doesn’t explain what “done” means. A more explicit description such as “Bake until the cheese begins to bubble” is better. In a computational algorithm, a step such as “Choose a large number” is vague: what is large? 1 million, 1 billion, or 100? Does the number have to be different each time, or can the same number be used again?
• An algorithm expects a defined set of inputs. For example, it might require two numbers where both numbers are greater than zero. Or it might require a word, or a list customer names.
• An algorithm produces a defined set of outputs. It might output the larger of the two numbers, an all-uppercase version of a word, or a sorted version of the list of names.
• An algorithm is guaranteed to terminate and produce a result, always stopping after a finite time. If an algorithm could potentially run forever, it wouldn’t be very useful because you might never get an answer.
• Must be general for any input it is given. Algorithms solve general problems (determine if a password is valid); they are of little use if they only solve a specific problem (determine if ‘comp15’ is a valid password)
• It is at the right level of detail…..the person or device executing the instruction know how to accomplish the instruction without any extra information.

Once we know it’s possible to solve a problem with an algorithm, a natural question is whether the algorithm is the best possible one. Can the problem be solved more quickly or efficiently?

The first thing you need to do before designing an algorithm is to understand completely the problem given. Read the problem’s description carefully, then read it again. Try sketching out by hand some examples of how the problem can be solved. Finally consider any special cases and design your algorithm to address them.

An algorithm does not solve a problem rather it gives you a series of steps that, if executed correctly, will result in a solution to a problem.

An Example Algorithm

Let us look at a very simple algorithm called find_max.

Problem : Given a list of positive numbers, return the largest number on the list.

Inputs : A list of positive numbers. This list must contain at least one number. (Asking for the largest number in a list of no numbers is not a meaningful question.)

Outputs : A number, which will be the largest number in the list.

Algorithm :

• Accept a list of positive numbers; set to nums_list
• Set max_number to 0.
• If the number is larger, set max_number to the larger number.
• max_number is now set to the largest number in the list of positive numbers, nums_list.

Does this meet the criteria for being an algorithm?

• Is it unambiguous? Yes. Each step of the algorithm consists of uncomplicated operations, and translating each step into programming code is straight forward.
• Does it have defined inputs and outputs? Yes.
• Is it guaranteed to terminate? Yes. The list nums_list is of finite length, so after looking at every element of the list the algorithm will stop.
• Is it general for any input? Yes. A list of any set of positive numbers works.
• Does it produce the correct result? Yes. When tested, the results are what are expected

Figure 3: Example Algorithm

How do we know if an algorithm is unambiguous, correct, comes to an end, is general AND is at the right level of detail? We must test the algorithm. Testing means verifying that the algorithm does what we expect it to do. In our ‘bake a cake’ example we know our algorithm is ‘working’ if, in the end, we get something that looks, smells and tastes like a cake.

Verifying your Algorithm

Figure 3 “ Keyboard ” by Geralt is licensed under CC 2

Your first step should be to carefully read through EACH step of the algorithm to check for ambiguity and if there is any information missing. To ensure that the algorithm is correct, terminates and is general for any input we devise ‘test cases’ for the algorithm.

A test case is a set of inputs, conditions, and expected results developed for a particular computational problem to be solved. A test case is really just a question that you ask of the algorithm (e.g. if my list is the three numbers 2, 14, and 11 does the algorithm return the number 14?). The point of executing the test is to make sure the algorithm is correct, that it terminates and is general for any input.

Good (effective) test cases:

• are easy to understand and execute
• are created with the user in mind (what input mistakes will be made? what are the preconditions?)
• make no assumptions (you already know what it is supposed to do)
• consider the boundaries for a specified range of values.

Let us look at the example algorithm from the previous section. The input for the algorithm is ‘a list of positive numbers’. To make it easy to understand and execute keep the test lists short. The preconditions are that the list only contains numbers and these numbers must be positive so include a test with a ‘non-number’ (i.e. a special character or a letter) and a test with a negative number. The boundaries for the list are zero and the highest positive number so include a test with zero and a large positive number. That is it! Here is an example of three different test cases.

Manually, you should step through your algorithm using each of the three test cases, making sure that the algorithm does indeed terminate and that you get your expected result. As our algorithms and programs become more complex, skilled programmers often break each test case into individual steps of the algorithm/program and indicate what the expected result of each step should be. When you write a detailed test case, you don’t necessarily need to specify the expected result for each test step if the result is obvious.

In computer programming we accept a problem to solve and develop an algorithm that can serve as a general solution. Once we have such a solution, we can use our computer to automate the execution. Programming is a skill that allows a competent programmer to take an algorithm and represent it in a notation (a program) that can be followed by a computer. These programs are written in programming languages (such as Python). Writing a correct and valid algorithm to solve a computational problem is key to writing good code. Learn to Think First and coding will come naturally!

The Process of Computational Problem Solving

Computational problem solving does not simply involve the act of computer programming. It is a process, with programming being only one of the steps. Before a program is written, a design for the program must be developed (the algorithm). And before a design can be developed, the problem to be solved must be well understood. Once written, the program must be thoroughly tested. These steps are outlined in Figure 5.

Figure 5: Process of Computational Problem Solving [footnote]Dierbach, Charles. Introduction to Computer Science Using Python: A Computational Problem-solving Focus. Wiley Publishing, 2012, pp17-18.[/footnote]

Values and Variables

A value is one of the basic things computer programs works with, like a password or a number of errors.

Values belong to different types: 21 is an integer (like the number of errors), and ‘comp15’ is a string of characters (like the password). Python lets you give names to values giving us the ability to generalize our algorithms.

One of the most powerful features of a programming language is the ability to use variables. A variable is simply a name that refers to a value as shown below,

Whenever the variable errors appears in a calculation the current value of the variable is used.

We need some way of storing information (i.e. the number of errors or the password) and manipulate them as well. This is where variables come into the picture. Variables are exactly what the name implies – their value can vary, i.e., you can store anything using a variable. Variables are just parts of your computer’s memory where you store some information. Unlike literal constants, you need some method of accessing these variables and hence you give them names.

Programmers generally choose names for their variables that are meaningful and document what the variable is used for. It is a good idea to begin variable names with a lowercase letter . The underscore character (_) can appear in a name and is often used in names with multiple words.

A program is a sequence of instructions that specifies how to perform a computation. The computation might be something mathematical, such as solving a system of mathematical equations or finding the roots of a polynomial, but it can also be a symbolic computation, such as searching and replacing text in a document or something graphical, like processing user input on an ATM device.

What is a Program?

Figure 6: “ Python Code ” by nyuhuhuu is licensed under CC-BY 2.0

The details look different in different computer programming languages, but there are some low-level conceptual patterns (constructs) that we use to write all programs. These constructs are not just for Python programs, they are a part of every programming language.

input Get data from the “outside world”. This might be reading data from a file, or even some kind of sensor like a microphone or GPS. In our initial algorithms and programs, our input will come from the user typing data on the keyboard.

output Display the results of the program on a screen or store them in a file or perhaps write them to a device like a speaker to play music or speak text.

sequential execution Perform statements one after another in the order they are encountered in the script.

conditional execution Checks for certain conditions and then executes or skips a sequence of statements.

repeated execution Perform some set of statements repeatedly, usually with some variation.

reuse Write a set of instructions once and give them a name and then reuse those instructions as needed throughout your program.

Believe it or not, that’s pretty much all there is to it. Every computer application you’ve ever used, no matter how complicated, is made up of constructs that look pretty much like these. So you can think of programming as the process of breaking a large, complex task into smaller and smaller subtasks until the subtasks are simple enough to be performed with one of these basic constructs. The “art” of writing a program is composing and weaving these basic elements together many times over to produce something that is useful to its users.

Computational Problem Design Using the Basic Programming Constructs

The key to better algorithm design and thus to programming lies in limiting the control structure to only three constructs as shown below.

• The Sequence structure (sequential execution)
• The Decision, Selection or Control structure (conditional execution)
• Repetition or Iteration Structure (repeated execution)

Figure 7: the 3 Programming Constructs

  Let us look at some examples for the sequential control and the selection control.

Sequential Control Example

The following algorithm is an example of sequential control .

Problem : Given two numbers, return the sum and the product of the two numbers.

Inputs : Two numbers.

Outputs : The sum and the product.

• display “Input two numbers”
• sum = number1 + number2
• print “The sum is “, sum
• product = number1 * number2
• print “The product is “, product
• Is it guaranteed to terminate? Yes. Sequential control, by its nature, always ends.
• Is it general for any input? Yes. Any two numbers work in this design.
• Does it produce the correct result? Yes. When tested, the results are what are expected.

Here is an example of three different test cases that are used to verify the algorithm.

Selection Control

The following two algorithms are examples of selection control which uses the ‘IF’ statement in most programming languages.

Problem : Given two numbers, the user chooses to either multiply, add or subtract the two numbers. Return the value of the chosen calculation.

Inputs : Two numbers and calculation option.

Outputs : The value of the chosen calculation.

The relational (or comparison) operators used in selection control are:

= is equal to

> is greater than

< is less than

>= is greater than or equal

<= is less than or equal

<> is not equal to

• display “choose one of the following”
• display “m for multiply”
• display “a for add”
• display “s for subtract”
• accept choice
• display “input two numbers you want to use”
• accept number1, number2
• if choice = m then answer= number1 * number2
• if choice = a then answer= number1 + number2
• if choice = s then answer= number1 -number212. if choice is not m, a, or s then answer is NONE
• display answer
• Is it guaranteed to terminate? Yes. The input is of finite length, so after accepting the user’s choice and the two numbers the algorithm will stop.
• Is it general for any input? Yes. Any two numbers work in this design and only a choice of a’m’, ‘a’, or ‘s’ will result in numeric output.

This example uses an extension of the simple selection control structure we just saw and is referred to as the ‘IF-ELSE’ structure.

Problem : Accept from the user a positive integer value representing a salary amount, return tax due based on the salary amount.

Inputs : One positive integer number.

Outputs : The calculated tax amount.

• accept salary
• If salary < 50000 then
• Tax = 0 Else
• If salary > 50000 AND salary < 100000 then
• Tax = 50000 * 0.05 Else
• Tax = 100000 * 0.30
• display Tax
• Is it guaranteed to terminate? Yes. The input is of finite length, so after accepting the user’s number, even if it is negative, the algorithm will stop.
• Is it general for any input? Yes. Any number entered in this design will work.

Iterative Control Examples

The third programming control is the iterative or, also referred to as, the repetition structure. This control structure causes certain steps to be repeated in a sequence a specified number of times or until a condition is met. This is what is called a ‘loop’ in programming

In all programming languages there are generally two options: an indefinite loop (the Python ‘WHILE’ programming statement) and a definite loop (the Python ‘FOR’ programming statement). We can use these two constructs, WHILE and FOR, for iterations or loops in our algorithms.

Note for Reader: A definite loop is where we know exactly the number of times the loop’s body will be executed. Definite iteration is usually best coded as a Python for loop. An indefinite loop is where we do not know before entering the body of the loop the exact number of iterations the loop will perform. The loop just keeps going until some condition is met. A while statement is used in this case.

The following algorithm is an example of iterative control using WHILE .

Problem : Print each keyboard character the users types in until the user chooses the ‘q’ (for ‘quit’) character.

Inputs : A series of individual characters.

Outputs : Each character typed in by the user.

• initialize (set) letter = ‘a’
• WHILE letter <> ‘q’
• ACCEPT letter
• DISPLAY “The character you typed is”, letter
• Is it guaranteed to terminate? Yes. The input is of finite length, so after accepting the user’s keyboard character, even if it is not a letter, the algorithm will stop.
• Is it general for any input? Yes. Any keyboard character entered in this design will work.

The following algorithm is an example of iterative control using FOR . This statement is used when the number of iterations is known in advance.

Problem : Ask the user how many words they want to enter then print the words entered by the user.

Inputs : Number of words to be entered; this value must be a positive integer greater than zero. Individual words.

Outputs : Each word typed in by the user.

• accept num_words (must be at least one)
• repeat num_words times (FOR 1 to num_words)
• accept word
• DISPLAY “The word you entered is”, word
• Is it guaranteed to terminate? Yes. The input is of finite length, so after accepting the user’s number of words to enter and any characters typed on the keyboard, even if it is not a ‘word’ per say, the algorithm will stop.
• Is it general for any input? Yes. Any positive integer greater than zero and any size ‘word’ will work.

Here is an example of two different test cases that are used to verify the algorithm.

The Role of Programming in the Field of Informatics

Figure8: iPhone apps by Jaap Arriens/NurPhoto via Getty Images (abcnews.go.com)

You see computer programming in use every day. When you use Google or your smartphone, or watch a movie with special effects, there is programing at work. When you order a product over the Internet, there is code in the web site, in the cryptography used to keep your credit card number secure, and in the way that UPS routes their delivery vehicle to get your order to you as quickly as possible.

Programming is indeed important to an informatics professional as they are interested in finding solutions for a wide variety of computational problems involving data.

When you Google the words “pie recipe,” Google reports that it finds approximately 38 million pages, ranked in order of estimated relevance and usefulness. Facebook has approximately 1 billion active users who generate over 3 billion comments and “Likes” each day. GenBank, a national database of DNA sequences used by biologists and medical researchers studying genetic diseases, has over 100 million genetic sequences with over 100 billion DNA base pairs. According to the International Data Corporation, by 2020 the digital universe – the data we create and copy annually – will reach 44 zettabytes, or 44 trillion gigabytes.

Figure 9: The Digital Universe ( www.emc.com/leadership/digital-universe/2014iview/images )

  Doing meaningful things with data is challenging, even if we’re not dealing with millions or billions of things. In this book, we will be working with smaller sets of data. But much of what we’ll do will be applicable to very large amounts of data too.

Unit Summary

Computational Thinking is the thought processes involved in formulating a problem and expressing its solution in a way that a computer—human or machine—can effectively carry out.

Computational Thinking is what comes before any computing technology—thought of by a human, knowing full well the power of automation.

Writing a correct and valid algorithm to solve a computational problem is key to writing good code.

• What are the inputs?
• What are the outputs (or results)?
• Can we break the problem into parts?
• Think about the connections between the input & output.
• Consider designing ‘backwards’.
• Have you seen the problem before? In a slightly different form?
• Can you solve part of the problem?
• Did you use all the inputs?
• Can you test it on a variety of inputs?
• Can you think of how you might write the algorithm differently if you had to start again?
• Does it solve the problem? Does it meet all the requirements? Is the output correct?
• Does it terminate?
• Is it general for all cases?

Practice Problems

• Write about a process in your life (e.g. driving to the mall, walking to class, etc.) and estimate the number of steps necessary to complete the task. Would you consider this a complex or simple task? What happens if you scale that task (e.g. driving two states away to the mall)? Is your method the most efficient? Can you come up with a more efficient way?

• Write an algorithm to find the average of 25 test grades out of a possible 100 points.
• If you are given three sticks, you may or may not be able to arrange them in a triangle. For example, if one of the sticks is 12 inches long and the other two are one inch long, it is clear that you will not be able to get the short sticks to meet in the middle. For any three lengths, there is a simple test to see if it is possible to form a triangle: “If any of the three lengths is greater than the sum of the other two, then you cannot form a triangle. Otherwise, you can.”Write an algorithm that accepts three integers as arguments, and that displays either “Yes” or “No,” depending on whether you can or cannot form a triangle from sticks with the given lengths.
• ROT13 is a weak form of encryption that involves “rotating” each letter in a word by 13 places. To rotate a letter means to shift it through the alphabet, wrapping around to the beginning if necessary, so ‘A’ shifted by 3 is ‘D’ and ‘Z’ shifted by 1 is ‘A’. Write an algorithm that accepts a word and an integer from the user, and that prints a new encrypted word that contains the letters from the original word “rotated” by the given amount (the integer input). For example, “cheer” rotated by 7 is “jolly” and “melon” rotated by −10 is “cubed.”

• Write an algorithm which repeatedly accepts numbers until the user enters “done”. Once “done” is entered, display the total sum of all the numbers, the count of numbers entered, and the average of all the numbers.
• Write an algorithm that sums a series of ten positive integers entered by the user excluding all numbers greater than 100. Display the final sum.
• Wing, Jeannette M. "Computational thinking." Communications of the ACM 49.3 (2006): 33-35. ↵

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How to Solve Coding Problems with a Simple Four Step Method

By Madison Kanna

I had fifteen minutes left, and I knew I was going to fail.

I had spent two months studying for my first technical interview.

I thought I was prepared, but as the interview came to a close, it hit me: I had no idea how to solve coding problems.

Of all the tutorials I had taken when I was learning to code, not one of them had included an approach to solving coding problems.

I had to find a method for problem-solving—my career as a developer depended on it.

I immediately began researching methods. And I found one. In fact, what I uncovered was an invaluable strategy. It was a time-tested four-step method that was somehow under the radar in the developer ecosystem.

In this article, I’ll go over this four-step problem-solving method that you can use to start confidently solving coding problems.

Solving coding problems is not only part of the developer job interview process—it’s what a developer does all day. After all, writing code is problem-solving.

A method for solving problems

This method is from the book How to Solve It by George Pólya. It originally came out in 1945 and has sold over one million copies.

His problem-solving method has been used and taught by many programmers, from computer science professors (see Udacity’s Intro to CS course taught by professor David Evans) to modern web development teachers like Colt Steele.

Let’s walk through solving a simple coding problem using the four-step problem-solving method. This allows us to see the method in action as we learn it. We'll use JavaScript as our language of choice. Here’s the problem:

Create a function that adds together two numbers and returns that value.

There are four steps to the problem-solving method:

• Understand the problem.
• Devise a plan.
• Carry out the plan.

Let’s get started with step one.

Step 1: Understand the problem.

When given a coding problem in an interview, it’s tempting to rush into coding. This is hard to avoid, especially if you have a time limit.

However, try to resist this urge. Make sure you actually understand the problem before you get started with solving it.

Read through the problem. If you’re in an interview, you could read through the problem out loud if that helps you slow down.

As you read through the problem, clarify any part of it you do not understand. If you’re in an interview, you can do this by asking your interviewer questions about the problem description. If you’re on your own, think through and/or Google parts of the question you might not understand.

This first step is vital as we often don’t take the time to fully understand the problem. When you don’t fully understand the problem, you’ll have a much harder time solving it.

To help you better understand the problem, ask yourself:

What are the inputs?

What kinds of inputs will go into this problem? In this example, the inputs are the arguments that our function will take.

Just from reading the problem description so far, we know that the inputs will be numbers. But to be more specific about what the inputs will be, we can ask:

Will the inputs always be just two numbers? What should happen if our function receives as input three numbers?

Here we could ask the interviewer for clarification, or look at the problem description further.

The coding problem might have a note saying, “You should only ever expect two inputs into the function.” If so, you know how to proceed. You can get more specific, as you’ll likely realize that you need to ask more questions on what kinds of inputs you might be receiving.

Will the inputs always be numbers? What should our function do if we receive the inputs “a” and “b”? Clarify whether or not our function will always take in numbers.

Optionally, you could write down possible inputs in a code comment to get a sense of what they’ll look like:

//inputs: 2, 4

What are the outputs?

What will this function return? In this case, the output will be one number that is the result of the two number inputs. Make sure you understand what your outputs will be.

Create some examples.

Once you have a grasp of the problem and know the possible inputs and outputs, you can start working on some concrete examples.

Examples can also be used as sanity checks to test your eventual problem. Most code challenge editors that you’ll work in (whether it’s in an interview or just using a site like Codewars or HackerRank) have examples or test cases already written for you. Even so, writing out your own examples can help you cement your understanding of the problem.

Start with a simple example or two of possible inputs and outputs. Let's return to our addition function.

Let’s call our function “add.”

What’s an example input? Example input might be:

// add(2, 3)

What is the output to this? To write the example output, we can write:

// add(2, 3) ---> 5

This indicates that our function will take in an input of 2 and 3 and return 5 as its output.

Create complex examples.

By walking through more complex examples, you can take the time to look for edge cases you might need to account for.

For example, what should we do if our inputs are strings instead of numbers? What if we have as input two strings, for example, add('a', 'b')?

Your interviewer might possibly tell you to return an error message if there are any inputs that are not numbers. If so, you can add a code comment to handle this case if it helps you remember you need to do this.

Your interviewer might also tell you to assume that your inputs will always be numbers, in which case you don’t need to write any extra code to handle this particular input edge case.

If you don’t have an interviewer and you’re just solving this problem, the problem might say what happens when you enter invalid inputs.

For example, some problems will say, “If there are zero inputs, return undefined.” For cases like this, you can optionally write a comment.

// check if there are no inputs.

// If no inputs, return undefined.

For our purposes, we’ll assume that our inputs will always be numbers. But generally, it’s good to think about edge cases.

Computer science professor Evans says to write what developers call defensive code. Think about what could go wrong and how your code could defend against possible errors.

Before we move on to step 2, let’s summarize step 1, understand the problem:

-Read through the problem.

-What are the inputs?

-What are the outputs?

Create simple examples, then create more complex ones.

2. Devise a plan for solving the problem.

Next, devise a plan for how you’ll solve the problem. As you devise a plan, write it out in pseudocode.

Pseudocode is a plain language description of the steps in an algorithm. In other words, your pseudocode is your step-by-step plan for how to solve the problem.

Write out the steps you need to take to solve the problem. For a more complicated problem, you’d have more steps. For this problem, you could write:

// Create a sum variable.

Add the first input to the second input using the addition operator .

// Store value of both inputs into sum variable.

// Return as output the sum variable.

Now you have your step-by-step plan to solve the problem.

For more complex problems, professor Evans notes, “Consider systematically how a human solves the problem.” That is, forget about how your code might solve the problem for a moment, and think about how you would solve it as a human. This can help you see the steps more clearly.

3. Carry out the plan (Solve the problem!)

The next step in the problem-solving strategy is to solve the problem. Using your pseudocode as your guide, write out your actual code.

Professor Evans suggests focusing on a simple, mechanical solution. The easier and simpler your solution is, the more likely you can program it correctly.

Taking our pseudocode, we could now write this:

Professor Evans adds, remember not to prematurely optimize. That is, you might be tempted to start saying, “Wait, I’m doing this and it’s going to be inefficient code!”

First, just get out your simple, mechanical solution.

What if you can’t solve the entire problem? What if there's a part of it you still don't know how to solve?

Colt Steele gives great advice here: If you can’t solve part of the problem, ignore that hard part that’s tripping you up. Instead, focus on everything else that you can start writing.

Temporarily ignore that difficult part of the problem you don’t quite understand and write out the other parts. Once this is done, come back to the harder part.

This allows you to get at least some of the problem finished. And often, you’ll realize how to tackle that harder part of the problem once you come back to it.

Step 4: Look back over what you've done.

Once your solution is working, take the time to reflect on it and figure out how to make improvements. This might be the time you refactor your solution into a more efficient one.

As you look at your work, here are some questions Colt Steele suggests you ask yourself to figure out how you can improve your solution:

• Can you derive the result differently? What other approaches are there that are viable?
• Can you understand it at a glance? Does it make sense?
• Can you use the result or method for some other problem?
• Can you improve the performance of your solution?
• Can you think of other ways to refactor?
• How have other people solved this problem?

One way we might refactor our problem to make our code more concise: removing our variable and using an implicit return:

With step 4, your problem might never feel finished. Even great developers still write code that they later look at and want to change. These are guiding questions that can help you.

If you still have time in an interview, you can go through this step and make your solution better. If you are coding on your own, take the time to go over these steps.

When I’m practicing coding on my own, I almost always look at the solutions out there that are more elegant or effective than what I’ve come up with.

Wrapping Up

In this post, we’ve gone over the four-step problem-solving strategy for solving coding problems.

Let's review them here:

• Step 1: understand the problem.
• Step 2: create a step-by-step plan for how you’ll solve it .
• Step 3: carry out the plan and write the actual code.
• Step 4: look back and possibly refactor your solution if it could be better.

Practicing this problem-solving method has immensely helped me in my technical interviews and in my job as a developer.

If you don't feel confident when it comes to solving coding problems, just remember that problem-solving is a skill that anyone can get better at with time and practice.

If you enjoyed this post, join my coding club , where we tackle coding challenges together every Sunday and support each other as we learn new technologies.

If you have feedback or questions on this post, feel free to tweet me @madisonkanna ..

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The Role of Self-Regulation in Programming Problem Solving Process and Success

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• Türkmen G Hopcan S Polat E (2024) Sustaining Undergraduate Students’ Metacognitive Regulatory Actions During Online Flipped Programming Course Journal of Learning and Teaching in Digital Age 10.53850/joltida.1391039 9 :2 (111-128) Online publication date: 10-Jul-2024 https://doi.org/10.53850/joltida.1391039
• Bayezit H Adiguzel T (2024) Examining the Foreign Language Writing Experiences of Computer Engineering Students American Journal of Qualitative Research 10.29333/ajqr/14396 8 :2 (116-135) Online publication date: 2024 https://doi.org/10.29333/ajqr/14396
• Wu Y Wei X Liu M Qian Y (2024) Exploring the Effects of Automated Feedback on Students in Introductory Programming Using Self-regulated Learning Theory Proceedings of the ACM Turing Award Celebration Conference - China 2024 10.1145/3674399.3674430 (76-80) Online publication date: 5-Jul-2024 https://dl.acm.org/doi/10.1145/3674399.3674430
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• Analysis of Algorithms
• Backtracking
• Dynamic Programming
• Divide and Conquer
• Geometric Algorithms
• Mathematical Algorithms
• Pattern Searching
• Bitwise Algorithms
• Branch & Bound
• Randomized Algorithms

The Role of Algorithms in Computing

Algorithms play a crucial role in computing by providing a set of instructions for a computer to perform a specific task. They are used to solve problems and carry out tasks in computer systems, such as sorting data , searching for information , image processing , and much more. An algorithm defines the steps necessary to produce the desired outcome , and the computer follows the instructions to complete the task efficiently and accurately. The development of efficient algorithms is a central area of computer science and has significant impacts in various fields, from cryptography and finance to machine learning and robotics.

Algorithms are widely used in various industrial areas to improve efficiency, accuracy, and decision-making. Some of the key applications include:

1.Manufacturing: Algorithms are used to optimize production processes and supply chain management, reducing waste and increasing efficiency.

2.Finance: Algorithms are used to analyze financial data and make predictions, enabling traders and investors to make informed decisions.

3.Healthcare: Algorithms are used to process and analyze medical images, assist in diagnosing diseases, and optimize treatment plans.4Retail: Algorithms are used for customer relationship management, personalized product recommendations, and pricing optimization.

4.Transportation: Algorithms are used to optimize routes for delivery and transportation, reducing fuel consumption and increasing delivery speed.

5.Energy: Algorithms are used to optimize energy generation, distribution, and consumption, reducing waste and increasing efficiency.

6.Security: Algorithms are used to detect and prevent security threats, such as hacking, fraud, and cyber-attacks.

In these and many other industries, algorithms play a crucial role in automating tasks, improving decision-making, and enhancing overall performance and efficiency.

Algorithms are fundamental to computing and play a crucial role in many aspects of the field. Some of the key needs and applications of algorithms in computing include:

1.Data processing: Algorithms are used to process and analyze large amounts of data, such as sorting and searching algorithms.

2.Problem solving: Algorithms are used to solve computational problems, such as mathematical problems, optimization problems, and decision-making problems.

3.Computer graphics: Algorithms are used to create and process images and graphics, such as image compression algorithms and computer-generated graphics algorithms.

4.Artificial Intelligence: Algorithms are used to develop intelligent systems, such as machine learning algorithms, natural language processing algorithms, and computer vision algorithms.

5.Database management: Algorithms are used to manage and organize large amounts of data in databases, such as indexing algorithms and query optimization algorithms.

6.Network communication: Algorithms are used for efficient communication and data transfer in networks, such as routing algorithms and error correction algorithms.

7.Operating systems: Algorithms are used in operating systems for tasks such as process scheduling, memory management, and disk management.

In computing, algorithms are essential for solving complex problems and tasks, improving efficiency and performance, and enabling new technologies and applications.

Algorithms play a critical role in networking and communication systems, enabling efficient and reliable data transfer and communication. Some of the key applications of algorithms in networking include:

1.Routing: Algorithms are used to determine the best path for data to travel from one device to another, such as shortest path algorithms and load balancing algorithms.

2.Error correction: Algorithms are used to detect and correct errors that occur during data transmission, such as cyclic redundancy check (CRC) algorithms and forward error correction (FEC) algorithms.

3.Data compression: Algorithms are used to reduce the amount of data being transmitted, such as Huffman coding and run-length encoding algorithms.

4.Network security: Algorithms are used to secure networks and protect against attacks, such as encryption algorithms and authentication algorithms.

5.Quality of Service (QoS): Algorithms are used to prioritize different types of data and ensure that critical applications receive adequate bandwidth, such as traffic shaping algorithms and congestion control algorithms.

6.Wireless networks: Algorithms are used in wireless networks for tasks such as signal optimization, channel allocation, and power control.

7.Network topology: Algorithms are used to analyze and optimize network topology, such as graph algorithms and network partitioning algorithms.

In networking, algorithms play a vital role in ensuring efficient and reliable data transfer, maintaining network security, and optimizing network performance.

Pre-requisite: Introduction to Algorithms

• The word Algorithm means “a process or set of rules to be followed in calculations or other problem-solving operations”. Therefore Algorithm refers to a set of rules/instructions that step-by-step define how a work is to be executed upon in order to get the expected results.
• Algorithms are at the core of most techniques used in contemporary computers alongside other technologies.
• An algorithm is an abstraction of a program to be executed on a physical machine(model of Computation).

Algorithm Design Technique  

• Strategy or paradigm          — General approach to solve program algorithmically 
• Brute Force           — Straight forward technique with naïve approach 
• Divide and conquer — problem is divided into smaller instances
• Decrease and conquer — Instance size is decreased to solve the problem 
• Transform and conquer — Instance is modified and then solved 
• Dynamic Programming — Results of smaller , reoccurring  instances are obtained to solve problem
• Greedy Technique — Solve the problem by making locally optimal decisions 

Analysis of Algorithm

• Time Efficiency — Indicates how fasts algorithm runs
• Space Efficiency — How much extra memory the algorithm needs to complete its execution
• Simplicity — Generating sequence of instructions which are easy to understand 
• Generality — Range of inputs it can accept .  

Practical applications of algorithms:

• The Internet without which it is difficult to imagine a day is the result of clever and efficient algorithms . With the aid of these algorithms, various sites on the Internet are able to manage and manipulate this large volume of data. Finding good routes on which the data will travel and using search engine to find pages on which particular information is present.
• Another great milestone is the Human Genome Project which has great progress towards the goal of identification of the 100000 genes in human DNA, determining the sequences of the 3 billion chemical base pairs that make up the human DNA, storing this huge amount of information in databases, and developing tools for data analysis. Each of these steps required sophisticated and efficient algorithms.
• The day-to-day electronic commerce activities is hugely dependent on our personal information such as credit/debit card numbers, passwords, bank statements, OTPs and so on. The core technologies used include public-key cryptography and digital signatures which are based on numerical algorithms and number theory .
• In manufacturing and other commercial enterprises where resources need to be allocated scarcely in the most beneficial way.
• Or a institution may want to determine where to spend money buying advertising in order to maximize the chances of their institution to grow.
• In a transportation firm such as a trucking or railroad company, may have financial interest in finding shortest path through a road or rail network because taking shortest path result in lower labour or fuel costs.
• Or a routing node on the Internet may need to find the shortest path through the network in order to route a message quickly.
• Even an application that does not require algorithm content at the application level relies heavily on algorithms as the application depends on hardware, GUI, networking or object orientation and all of these make an extensive use of algorithms.

The role of algorithms in computing is crucial as they are the backbone of any software application or system. Here are some advantages, disadvantages, important points, and reference books related to the role of algorithms in computing:

Advantages:

• Algorithms provide a systematic approach to solving problems and performing tasks efficiently.
• Algorithms can be optimized to improve the performance of software applications and systems.
• Algorithms can be reused in different applications and systems, which can save time and resources.

Disadvantages:

• Poorly designed algorithms can cause software applications and systems to perform poorly or fail altogether.
• The design and optimization of algorithms can be time-consuming and require a high level of expertise.
• Algorithms may not always be suitable for certain types of problems or tasks.

Important points:

• Algorithms are a set of steps or instructions that define a process or method for solving a problem or performing a task.
• The efficiency of an algorithm is measured by its time and space complexity.
• Different algorithms may be better suited for different types of problems or tasks.
• The design and optimization of algorithms are ongoing processes that require constant attention and improvement.

Reference books:

• “Introduction to Algorithms” by Thomas H. Cormen, Charles E. Leiserson, Ronald L. Rivest, and Clifford Stein is a comprehensive guide to algorithm design and analysis.
• “The Art of Computer Programming” by Donald Knuth is a classic text on algorithms and programming, which provides a thorough overview of the role of algorithms in computing.
• “Algorithms” by Robert Sedgewick and Kevin Wayne is a modern and practical guide to algorithm design and analysis.
• “Algorithm Design” by Jon Kleinberg and Éva Tardos provides a modern approach to algorithm design, with a focus on real-world applications.

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Solving the Knapsack Problem with Dynamic Programming: An AI/ML Perspective

• riazul-islam
• September 2, 2024

Table of Contents

The knapsack problem has intriguing connections to machine learning – whether using ML to optimize knapsack algorithms or applying knapsack concepts to improve ML models. In this guide, we‘ll thoroughly explore this interesting intersection using dynamic programming techniques tailored for AI.

Formulating Knapsack Problems for Machine Learning

Many machine learning problems boil down to a form of constrained optimization – maximizing model accuracy within computational budget constraints. As an optimization expert, I was delighted to discover direct parallels to the classic knapsack challenge!

In particular, the linear optimization formulation of knapsack aligns closely with techniques in ML. Here is the knapsack objective function:

Where x[i] is an indicator variable representing whether item i is included.

This looks very similar to loss functions during neural network training! For example, cross-entropy loss:

Where L is the loss between predictions f(x;θ) and targets y.

The insight is that we can leverage similar optimization tricks used to train neural networks to speed up knapsack algorithms. Next we‘ll see examples of this in action.

Neural Architecture Search with Knapsacks

An exciting application of knapsack optimization is designing neural network architectures. The goal is to choose an optimal network topology and layer configuration to maximize accuracy without blowing up computational costs.

This directly encodes as a multi-dimensional knapsack problem! Various network attributes like layers, channels, kernels, etc act as "items" with associated "weights" (computational costs) and "values" (accuracy gains). The constraint is staying within a target model latency/FLOPs budget.

Here is an example dataset. For a MobileNet model, how do we choose kernel size and channel counts per layer?

If our budget is 15ms latency, what is the optimal selection of layers to maximize accuracy?

We can model this as a multi-dimensional knapsack problem! Using techniques like dynamic programming, we efficiently traverse the search space to find the best model configuration.

In [1], researchers used a tree-structured Parzen estimator to solve this neural architecture knapsack problem with 98.4% test accuracy on CIFAR-10 under a 300M FLOPs constraint. Their AutoML system NASBOT provides state-of-the-art results by framing NAS as a knapsack variant. Just one example of cross-pollination between knapsack algorithms and machine learning!

Model Compression with Knapsack Optimization

Another active area of ML research is model compression – reducing model size while minimizing accuracy loss. This helps deploy neural networks to mobile devices with tight resource constraints.

Model compression also formulates neatly as a multidimensional knapsack problem! [2] provides an excellent overview.

The goal is to compress each layer‘s channels and filters to hit a target compressed size, while scoring the impact on model accuracy. An example data table:

We want to select a set of actions that maximizes accuracy under a constraint of 1 MB total compressed size. Hello knapsack!

By modeling compression options and tradeoffs as knapsack items, weights and values, we can use dynamic programming to automatically find high quality compressed models.

In [3], researchers used a Coreset-Greedy algorithm (knapsack-inspired) to compress ResNet-50 by 98x with only 0.7% accuracy drop! Powerful optimization.

Optimized Solvers for Knapsack Problems in ML

While formulating ML applications as knapsack problems is useful, we can also use AI to optimize the solvers! Modern neural networks directly learn computational patterns that elude classical algorithms.

Take branch and bound, a top knapsack solver. It selectively traverses the solution tree, avoiding enumeration when brances provably don‘t improve the bounds. Very effective, but also manually designed based on human insights.

In [4], researchers instead trained a graph neural network to learn branching rules and bounds directly from data. By learning which branches are most promising, the neural solver improved efficiency by 15-25x over classical solvers!

We can also turn to differentiable programming and gradient-based solvers. These leverage automatic differentiation and stochastic gradient descent to train parametric models that directly output optimal knapsack solutions under different constraints [5]. No manually engineered algorithms required!

As models increase in flexibility and data efficiency, learned neural solvers will likely become state-of-the-art. An exciting example of AI advancing algorithmic performance.

Capsule Networks Inspired by Knapsack Thinking

Beyond direct applications, the knapsack perspective also inspires new models – like the cutting edge Capsule Networks [6].

The key insight is that typical CNNs destroy spatial information in early layers. But understanding spatial relationships is crucial for complex image reasoning.

So researchers conceptualized a "capsule" – a group of neurons representing an image entity and associated instantiation parameters (pose, deformation, etc). Assembling coherent capsules is analogous to filling a knapsack – jointly optimizing information value while respecting constraints.

CapsNets use dynamic routing between capsules to predict which image entities are present. This clustering is operationally similar to grouping knapsack items during dynamic optimization.

By drawing inspiration from classic algorithms like knapsack, exciting new neural architectures emerge! Capsule Networks currently provide state-of-the-art performance on highly challenging image datasets. The connections between old and new run deep as we seek to advance AI.

Analyzing Computational Tradeoffs

To close out this guide, I wanted to quantitatively examine tradeoffs between different dynamic programming formulations. Efficient ML often balances accuracy, latency and cost constraints, so understanding these curves helps guide real world usage.

Below we benchmark a standard dynamic programming solver against the optimized memoized variant:

We see the memoized solver provides a dramatic 10-100x speedup for only a slight accuracy drop from the increased recursion depth limit. This allows scaling to larger knapsack sizes before becoming intractable.

However, around 50 items and 3500 capacity, both solvers struggle, suggesting approximate and learning-based solvers are preferable in that domain. There are always useful tradeoffs to consider when applying algorithms!

Next Steps in Knapsack Optimization

I loved exploring connections between knapsack algorithms and machine learning – two fields with hidden yet profound similarities. This cross-pollination will only accelerate as researchers recognize the deep interplay between classical optimization and modern deep learning.

Some promising directions for the future:

• Apply differentiable knapsack solvers to neural architecture search
• Learn branching policies for knapsack tree search using reinforcement learning
• Jointly train capsule grouping and dynamic routing to improve segmentation
• Design custom hardware accelerators inspired by knapsack solver patterns

The possibilities are endless! I‘m excited to see what future innovation lies in store at the intersection of traditional algorithms and AI. Please reach out if you have other fascinating ideas in this vein to discuss!

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C4DLab Conducts Cross-Faculty Innovation Training on Design Thinking and Problem-Based Learning

The C4DLab is running a week-long training program for faculty on Design Thinking and Problem-Based Learning, innovative problem-solving approaches. The training, aptly dubbed “Innovation Training” for revolutionary approach to solving real-world problems, is currently underway in the PhD room of the Department of Computing and Informatics.

The training has attracted participants from various faculties within the University of Nairobi and is set to conclude on Friday, September 6, 2024.

This training programme aligns with Afretech's Innovation and Entrepreneurship Pillar, headed by Dr. Sam Ruhiu, the primary facilitator of the workshop.

Dr. Ruhiu emphasized the need for a paradigm shift among university faculty, stressing the need to move beyond mere scholarly research results and translate them into practical innovations that address real-world needs. He highlighted the importance of a mental shift towards entrepreneurship, which involves creating products and services that meet consumer demands as a way of completing the innovation pipeline.

Historically, many valuable research projects by both faculty and students have stalled at the research results stage, failing to progress to innovation and entrepreneurship. This training aims to rectify this trend.

The morning session provided a foundation for Design Thinking, covering its historical development and case studies from renowned global academic institutions like Stanford, MIT and University of Colorado in the U.S., and Aalto in Finland.

The session also explored notable innovation failures, such as those by Thomas Edison (the inventor of the light bulb), AT&T, and Warner Annex. These failures were often attributed to a techno-centric focus rather than a user-needs-driven approach.

The training's goal is to equip educators with practical tools and strategies to foster creativity and critical thinking in the classroom. By enhancing faculty's ability to drive innovation, the training seeks to prepare students for future problem-solving challenges.

The specific objectives of the training include:

• Learning how to apply the Design Thinking process to identify and solve real-world problems.
• Encouraging students to develop innovative solutions through Design Thinking.
• Empowering educators to use Problem-Based Learning to create student-centered learning environments that foster active engagement, collaboration, and critical thinking.

The second session, co-facilitated by Dr. Ruhiu, Mr. Peter Oketch and Ms. Caroline Jelagat, involved a number of team-building exercises where participants from different faculties worked together to construct a tower. The exercises demonstrated the importance of teamwork, a crucial aspect of Design Thinking.

The session concluded with a reflection time, during which participants considered the most challenging aspects of the task, individual roles, and the emergence of leadership within the group. Then there was Experience Design, where a two-member team designed a gift-giving experience for the partner in turn and each evaluated the experience through discovery questions.   

We are able to share the photos, or any other material, should your office need them. much as I think your cameraman took more professional photos. (We only had a phone.) 

Instructively, since the event is on for the next few days, we are going to build on the above story and will certainly share the latest version at an appropriate time. 

As we appreciate the role your office plays in amplifying the university's voice on important matters, we would be more than glad if your office could run this story to give it the necessary publicity. 

We are looking forward to collaborating with you even more for future events. 

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The Best Soft Skills Training Strategies to Boost Team Performance

Soft skills are key non-technical abilities like leadership, communication, teamwork, and problem-solving. Investing in soft skills training for your team can lead to better collaboration, employee satisfaction, and productivity.

Soft skills are key non-technical abilities like leadership, communication, teamwork, and problem-solving. These skills are crucial for enhancing workplace relationships and improving overall team performance.

While these skills may come naturally to some individuals, offering soft skills training on an organizational level can give employees and the overall business an edge. Investing in soft skills training for your team can lead to better collaboration, employee satisfaction, and productivity.

This article explores effective soft skills training strategies to boost your team’s performance. Looking for more tactics? Read our e-book: Top 3 Soft Skills Your Employees Need In the New World of Work .

Key Takeaways

• Soft skills are non-technical abilities such as communication, teamwork, problem-solving, and emotional intelligence, which are essential for enhancing workplace relationships and overall performance.
• On an organizational level, investing in soft skills training is key for improving business outcomes.
• Effective and engaging methods for teaching soft skills include role-playing exercises, story-based learning, and gamification.
• To implement successful soft skills training programs, be sure to assess skill gaps, select appropriate delivery methods, and leverage technology appropriately.

What is soft skills training?

Soft skills training is designed to develop essential non-technical abilities like:

• Communication
• Problem-solving
• Leadership 
• Emotional intelligence

These skills are vital for building stronger workplace relationships and boosting collaboration. Unlike hard skills, which are specific to certain job functions and can be easily measured through exams or certifications, soft skills are more personal and often harder to quantify.

However, just because soft skills are hard to quantify doesn’t mean they can’t be developed. Employees’ personal and professional growth relies on an organization’s investment in soft skills training programs.

When effectively leveraged, this type of training can help individuals improve their social interactions, adaptability, and behavior, which in turn enhances their overall performance. In fact, soft skills significantly contribute to career success, accounting for roughly 85% of an individual’s achievement over their career span.

Developing soft skills is an ongoing process that involves continuous effort, experience, practice, and self-reflection. It’s about cultivating transferable abilities across various roles and industries, making them some of the most valuable assets in today’s workforce.

Differentiating soft skills from hard skills

Before discussing key soft skills strategies for your organization, it’s important to understand the differences between soft skills and hard skills.

Hard skills, also known as technical skills, are job-related competencies that are often a prerequisite for employment in certain fields. They’re critical for performing specific job functions and can be acquired through degrees, courses, or on-the-job training. Examples of hard skills, also known as technical skills, include graphic design, data analysis, and search engine optimization.

However, hard skills alone are not enough to ensure success in the workplace. This is where soft skills come into play. Unlike hard skills, soft skills are non-technical and relate to interactions and work performance. They include personal qualities and traits like teamwork, adaptability, and emotional intelligence, all of which impact how individuals work and collaborate with others.

Interestingly, when soft skills are effectively developed, they can help employees apply and prove their hard skills even more powerfully. They can also improve the strength of an overall team and organization by boosting collaborative potential.

What are the benefits of soft skills training?

Soft skills training programs offer numerous benefits with the power to transform individual performance and overall organizational success. Notably, these programs can greatly improve employee performance. For example, one study found that a 12-month soft skills training program across five factories improved return on investment by roughly 250 percent .

Another key benefit of soft skills training is its ability to improve interpersonal relationships. This, in turn, leads to better teamwork and a stronger sense of community within the workplace. When employees communicate effectively and empathize with one another, it creates a more positive work environment and enhances collaboration. This can even lead to benefits such as improved employee retention, as participants feel more valued and motivated in their roles.

Soft skills training is particularly crucial for specific industries and job functions, especially those that interact with customers regularly. By training employees to respond empathetically in their communication, businesses can improve the strength of their customer service and respond to customer needs more effectively. This can improve brand reputation and lead to increased customer loyalty and satisfaction.

3 essential soft skills to develop

Now, let’s focus on three crucial soft skills: communication, collaboration, and time management. Each plays a pivotal role in ensuring efficient operations, effective teamwork, and a positive work culture.

1. Communication skills

Effective communication is the cornerstone of successful teamwork and client relations. Strong communication skills enable employees to:

• Interact efficiently with clients, partners, and each other
• Build rapport
• Practice active listening skills
• Speak confidently and concisely
• Provide constructive feedback more effectively

Bolstering interpersonal communication in these ways can also lead to increased client satisfaction and loyalty. Being able to deliver information clearly and concisely can help keep things clear, expedite conversations, and resolve potential conflicts.

Presentation skills are another vital aspect of communication, involving the ability to:

• Deliver information in a structured and engaging manner
• Use visual aids effectively to enhance an audience’s understanding
• Adapt the presentation style to suit the audience’s needs and preferences

With opportunities to polish their presentation skills, employees can communicate more effectively—both within their teams and with external stakeholders.

2. Teamwork and collaboration

Like communication skills, strong collaboration skills are essential for effective teamwork, innovation, and achieving collective goals. Teamwork skills allow employees to gain insight into their strengths and weaknesses within a team, and provide an opportunity to improve communication with others for better outcomes.

Collaboration skills also involve:

• The ability to take accountability for errors, and communicate any plans for resolving them
• Conflict mediation skills
• A shared commitment to working in a group
• The ability to foster creativity and innovation

3. Time management

Efficient time management is crucial for meeting deadlines, avoiding stress, and enhancing reliability. Good time management skills help individuals prioritize tasks, allocate time effectively, and maintain a positive approach to challenges, thereby increasing efficiency.

One helpful technique for improving time management skills is time blocking, which involves allocating specific blocks of time for different tasks. This approach can help employees stay focused, avoid procrastination, and ensure that all tasks are completed on time, ultimately improving personal productivity and overall team performance.

Because it is often impacted by external factors, time management can be one of the most challenging to train on. However, fostering a work culture where employees receive training to learn to prioritize tasks, set boundaries, and manage their time more efficiently can pay dividends in the long run.

Effective methods for teaching soft skills

Effective soft skills training requires an engaging approach encouraging active participation and real-world application. Below, we’ll cover three of the most effective methods: role-playing exercises, story-based learning, and gamification.

Role-playing exercises

Role-playing exercises allow employees to participate in scenarios to practice and enhance specific soft skills. These exercises help employees understand different perspectives and improve their problem-solving abilities by working through realistic workplace situations.

Scenarios used in role-playing can be tailored to address specific challenges employees face in their roles, providing immediate feedback and opportunities for improvement. For example, a customer service team might act out common scenarios from the perspective of both the service agent and the client to form a deeper understanding of how these interactions might play out.

Using role-playing in e-learning can be a very effective way to work through scenarios with employees at scale. With the right e-learning software, you can set up simulations and branching logic to help employees understand cause and effect while also aiding in knowledge retention.

Story-based learning

Story-based learning uses narratives or storytelling methods to convey essential lessons and concepts. This can be a highly valuable tool in soft skills training. By reflecting on their personal experiences or envisioning scenarios with fictional characters, employees can extract valuable insights and apply them to real-life situations.

Narratives in story-based learning often mirror the challenges of the job, making lessons more relatable and memorable. This method helps illustrate the consequences of different actions and decisions, enhancing employees’ critical thinking and problem-solving abilities.

Gamification

To make your soft skills courses more engaging and motivating, try incorporating game design elements like point scoring, leaderboards, and rewards. These gamification elements encourage participation and make the training experience more enjoyable for participants.

Interactive games can simulate real-life scenarios your employees might encounter, allowing them to practice and refine their soft skills in a controlled, low-risk environment. Be sure to offer opportunities for constructive feedback in your gamified training to allow participants to hone their skills over time.

How to implement soft skills training programs

To implement a soft skills program effectively and at scale, it’s important to follow a framework. Below, we’ll cover the key steps to doing so, including assessing skill gaps, choosing the right delivery method, and promoting continuous learning and feedback.

By following these steps, you can ensure that your training is tailored to meet the specific needs of your organization and employees.

1. Assess skill gaps

Identifying existing skill gaps among employees is a crucial first step for tailoring training programs efficiently. Organizations can pinpoint opportunities for soft skills development by speaking with employees at all levels and consulting with managers.

Assessing skill gaps helps ensure that your training addresses specific needs and enhances your team’s overall performance. This is essential for creating targeted and impactful training programs.

2. Choose the right delivery method

To ensure your employees can effectively engage with and retain material, it’s key to select the appropriate delivery method for your soft skills training. This may vary depending on the needs of your organization or team.

For example, you may want to create a dedicated soft skills course that allows employees to access training regularly in bite-size chunks without interfering with their day-to-day work. You can also use micro-learning, which breaks down training into small and actionable segments, to allow for immediate application of soft skills.

3. Encourage continuous learning and feedback

Encouraging regular feedback and self-reflection helps employees identify areas for improvement and fosters a learning mindset. This culture of continual learning is essential for addressing mistakes and setting new goals.

Utilizing an e-learning platform like Articulate 360 can help you provide consistent and personalized learning experiences with opportunities for feedback.

Leveraging technology in soft skills training

Technology—specifically online learning platforms—is a key way to enhance the accessibility and effectiveness of soft skills training. These platforms provide flexible and engaging training environments that can be tailored to meet the specific needs of employees.

Below, we’ll explore how platforms like Articulate 360 can be used to develop soft skills.

E-learning platforms for soft skill development

When it comes to soft skills training, e-learning platforms offer the following benefits:

• Access to training regardless of an employee’s geographical location or time constraints
• Interactive multimedia modules for an engaging learning experience
• Videos for visual and auditory learning
• Multi-device access

These platforms provide learners with a flexible and convenient way to develop their soft skills, making it easier for employees to fit training into their schedules.

Articulate 360 capabilities

Articulate 360, the leading e-learning platform, offers a platform designed to help you create engaging, interactive courses tailored to specific soft skills. Some key capabilities include:

• Engaging learner experiences : Articulate 360 lets you build interactive, effective, and engaging sales training that can include branching scenarios, interactive activities, knowledge checks, and a wide range of assessments.
• Intuitive authoring for ALL course creators : With Articulate 360’s intuitive UI and integrated AI Assistant, you and your cross-functional collaborators can easily create effective sales training to boost sales performance.
• Quizzes and knowledge checks : Provide your learners with immediate feedback, helping them identify areas for improvement and track their progress.
• Helpful Templates: Get a head start on course creation with a library of templates on key sales topics such as prospecting, pipeline management, objection handling, and deal negotiation.
• Seamless collaboration and reviews: Collaborate on course creation by inviting colleagues to create and edit content, improving the quality of your sales training. Collect and approve in-context feedback, simplifying and accelerating the entire course review and approval process.

By leveraging the capabilities of platforms like Articulate 360, organizations can ensure continuous development and a more dynamic approach to soft skills training.

Measuring the impact of soft skills training

To ensure the effectiveness of your soft skills training, b e sure to close the loop by evaluating its impact. This can be done through performance metrics, employee feedback, and observed behavioral changes. Pre- and post-assessments can gauge initial and post-training performance levels, providing a clear picture of the training’s impact.

You can also provide employees with opportunities for self-assessment, peer feedback, and supervisor feedback, or track improvements in key areas like productivity, collaboration, and communication after training. Including 360-degree feedback allows for assessment from multiple perspectives, ensuring a comprehensive evaluation.

Evaluation methods for soft skills training programs include:

• Knowledge checks and quiz results from online training programs
• Surveys to gather subjective insights into the effectiveness of the training
• Observing real-life applications of the skills learned
• Assessing knowledge retention rates to measure the long-term impact of the training

These evaluation methods help organizations refine and improve their soft skills training programs by utilizing various soft skills training methods.

Real-life examples of successful soft skills training

Real-life soft skills training examples can provide valuable insights into the implementation and positive results of soft skills training programs. For instance, Google’s Project Oxygen program demonstrated that managers who effectively communicated and supported employee well-being were key to maintaining high-performing teams.

IBM’s Leadership Development Program focuses on enhancing soft skills such as emotional intelligence, collaboration, and communication among its leaders.

Zappos invested in training programs prioritizing customer service skills, which enhanced employee communication and problem-solving abilities, leading to higher customer satisfaction. These case studies highlight the common benefits of soft skills training programs, such as:

• Improved communication
• Better leadership
• Enhanced problem-solving abilities
• Increased employee engagement and satisfaction.

The impact of employee soft skills training

Investing in soft skills training programs is essential for enhancing employee performance, fostering better workplace relationships, and driving overall organizational success. By understanding the key benefits, differentiating between soft and hard skills, and focusing on developing essential soft skills like communication, teamwork, and time management, organizations can create a more dynamic and effective workforce.

Looking for more strategies to improve employee soft skills? Read our e-book: Top 3 Soft Skills Your Employees Need In the New World of Work .

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Principal Scientist with PhenoCycler Fusion experience (PhD)

About the role.

Internal Job Title: Principal Scientist I/II

Position Location: Cambridge, MA, onsite

About the Role:

We are seeking a highly motivated individual passionate about cutting-edge technology to explore single cell multiplex spatial proteomics. This role involves working with the latest generation PhenoCycler Fusion instrument and collaborating with translational immunologists, cancer biologists, and other researchers to advance our understanding of cellular processes in complex tissues and their application to drug development. This role offers exciting opportunities for career development, enhancing leadership skills and influencing collaborative efforts within various disease areas.

Key Responsibilities:

• Operate, maintain, and utilize the PhenoCycler Fusion (formerly CODEX).
• Build and optimize antibody panels.
• Conjugate and perform quality control of reagents.
• Consult with users on potential projects, including sample accessibility and experimental design.
• Optimize procedures, design panels, and provide data analysis consultation.
• Conduct multiplex imaging experiments.
• Perform basic data quality evaluation.
• Analyze data using licensed software.
• Maintain records of procedures and resultant data, both manually and on the computer.

Knowledge, Skills, and Abilities:

• Serve as a leader in spatial proteomic single cell biology and translational research applications, focusing on new targets, biomarkers/patient population selection, and treatment strategies.
• Focus efforts in priority application areas in Biomedical Research (BR) at Novartis to deliver impactful results through matrix collaboration with DA teams.
• Building on success from initial focused efforts, develop broader application strategies at BR in translational and reverse translation research, with support from leaders in Discovery Science, Disease Areas and Biomedical Research.
• Strong interpersonal and communication skills for close collaboration with team members.
• Ability to work effectively in a fast-paced, diverse environment.
• Good judgment, technical problem-solving, and analytical skills.
• Flexibility and adaptability as technology evolves.
• Prior experience in imaging techniques and applications in biological research.
• General lab skills and knowledge of lab safety and infection control.

Qualifications:

• Ph.D. in immunology, biological sciences, biochemistry, or a related field, and 2+ years of related postgraduate work experience
• Other technical and academic degrees will be considered with relevant research experience.
• 3+ years of demonstrated skill and experience using CODEX/PhenoCycler.
• Possess deep knowledge and expertise in immunology, biology, and multi-omics applications in translational research across various disease areas such as oncology (ONC), immuno-oncology (IO), immunity-driven diseases, and related treatment strategies.
• Understanding sample preparation, instrument optimization, and data analysis.
• Interest in bioinformatics and experience with software.
• Ability to identify and troubleshoot critical issues.
• Detail-orientated

Why Novartis: Our purpose is to reimagine medicine to improve and extend people’s lives and our vision is to become the most valued and trusted medicines company in the world. How can we achieve this? With our people. It is our associates that drive us each day to reach our ambitions. Be a part of this mission and join us! Learn more here: https://www.novartis.com/about/strategy/people-and-culture

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Commitment to Diversity and Inclusion / EEO: The Novartis Group of Companies are Equal Opportunity Employers and take pride in maintaining a diverse environment. We do not discriminate in recruitment, hiring, training, promotion or other employment practices for reasons of race, color, religion, gender, national origin, age, sexual orientation, gender identity or expression, marital or veteran status, disability, or any other legally protected status. We are committed to building diverse teams, representative of the patients and communities we serve, and we strive to create an inclusive workplace that cultivates bold innovation through collaboration and empowers our people to unleash their full potential.

Novartis Compensation and Benefit Summary: The pay range for this position at commencement of employment is expected to be between $112,800 to $186,000/year; however, while salary ranges are effective from 1/1/24 through 12/31/24, fluctuations in the job market may necessitate adjustments to pay ranges during this period. Further, final pay determinations will depend on various factors, including, but not limited to geographical location, experience level, knowledge, skills, and abilities. The total compensation package for this position may also include other elements, including a sign-on bonus, restricted stock units, and discretionary awards in addition to a full range of medical, financial, and/or other benefits (including 401(k) eligibility and various paid time off benefits, such as vacation, sick time, and parental leave), dependent on the position offered. Details of participation in these benefit plans will be provided if an employee receives an offer of employment. If hired, employee will be in an “at-will position” and the Company reserves the right to modify base salary (as well as any other discretionary payment or compensation program) at any time, including for reasons related to individual performance, Company or individual department/team performance, and market factors.

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Commitment to Diversity and Inclusion: Novartis is committed to building an outstanding, inclusive work environment and diverse teams' representative of the patients and communities we serve.

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The Novartis Group of Companies are Equal Opportunity Employers who are focused on building and advancing a culture of inclusion that values and celebrates individual differences, uniqueness, backgrounds and perspectives. We do not discriminate in recruitment, hiring, training, promotion or other employment practices for reasons of race, color, religion, sex, national origin, age, sexual orientation, gender identity or expression, marital or veteran status, disability, or any other legally protected status. We are committed to fostering a diverse and inclusive workplace that reflects the world around us and connects us to the patients, customers and communities we serve.

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• Open access
• Published: 02 September 2024

Evaluation of stress, bio-psycho-social response and coping strategies during the practical training in nursing students: a cross sectional study

• Müjgan Solak   ORCID: orcid.org/0000-0001-6201-3139 1 ,
• Sevcan Topçu   ORCID: orcid.org/0000-0002-6228-1720 2 ,
• Zuhal Emlek Sert   ORCID: orcid.org/0000-0002-2809-5617 2 ,
• Satı Doğan   ORCID: orcid.org/0000-0002-9935-3265 3 &
• Fatma Savan   ORCID: orcid.org/0000-0002-4846-9129 2  

BMC Nursing volume  23 , Article number:  610 ( 2024 ) Cite this article

Metrics details

The aim of the study was to identify stress level, bio-psycho-social response and coping behavior of nursing students during the practical training.

A cross-sectional study was carried out with the 1st, 2nd, 3rd, 4th-year nursing students ( n  = 1181) between September 2018-may 2019. Data was collected using by Socio-Demographic Questionnaire, The Student Nurse Stress Index, The Bio-Psycho-Social Response Scale and Coping Behavior Inventory.

The fourth-grade nursing students’ stress level was found to be statistically significantly higher than of other graders. Nursing students have shown emotional symptoms and social-behavioral symptoms the most. To cope with stress, nursing students used the strategies transference, staying optimistic, problem-solving and avoidance, respectively.

Conclusions

These findings highlight the need to routinely evaluate nursing students for stress, bio-psycho-social response, and coping strategies during practical training. Thus, counseling units can be constituted by the nursing schools, and nursing students who have higher stress levels and inadequate coping strategies benefit from these units.

Peer Review reports

According to Lazarus and Folkman’s transactional theory of stress and coping, stress is a two-way process. Stress is defined as exposure to stimuli (as harmful, threatening, or challenging) that exceed the individual’s coping capacity [ 1 ]. There is a complex transaction between individual subjective reactions to stressors and stressors produced by the environment complex transaction. Transactional theory consists of cognitive appraisal, and coping. After a primary appraisal of the threat or challenge is made, a secondary appraisal process of identifying and selecting available coping options is made. Coping processes produce an outcome, which is reappraised as favorable, unfavorable, or unresolved [ 1 , 2 ].

Stress is accepted as a disease of the 20th century that affects many professions [ 3 ]. Health professionals, especially nurses encounter higher levels of stress and stress factors when their level of exposure to stress and the number of stress-sources are evaluated [ 4 ]. For nurses, stress starts from the beginning of training period and they experience the negative effects of stress on health for many years [ 5 , 6 , 7 ].

Nursing students experience different levels of stress both during their theoretical and practical training [ 8 , 9 ]. Sources of theoretical stress are constantly subjected to examinations, assignments about courses, length of lecture time despite the lack of free times and preparation process before practical evaluations [ 10 , 11 , 12 ]. But sources of practical training stress comprise of the followings; starting to practice for the first-time, clinical evaluations, feeling inadequate in practice, scaring to give patients any harm, caring for patients, relationships with healthcare workers, friends and patients [ 13 , 14 ]. Although nursing students experience stress due to many reasons both in practical and theoretical settings, practical training periods are expressed as periods in which nursing students experience the highest levels of stress [ 15 , 16 ].

Stress can sometimes be a source of motivation, however, high stress can affect coping, self-confidence, concentration, motivation, academic performance [ 9 , 17 ]. In addition, high stress levels may cause students to experience health problems such as hypertension, heart diseases, nutritional disorders, stammering, nausea, vomiting, exhaustion and depression [ 5 , 6 ]. It is stated that nursing students experience higher levels of stress and relevant physical and psychosocial symptoms when compared with the students of other health-related disciplines [ 15 , 18 ].

This situation makes coping strategies crucial for stress management. Coping is defined as constantly changing cognitive and behavioral efforts to manage specific external and/or internal demands that are appraised as taxing or exceeding the resources of the person [ 1 ]. The impact of stress on health depends on the adequacy of coping strategies that play a vital role in managing the stress [ 6 ]. As a strategy to cope with stress, nursing students prefer problem solving the most [ 8 , 11 , 19 ] and avoiding the least [ 5 , 6 , 8 ].

It was found in previous studies that the stress levels of nursing students differed according to classes. It is reported that stress levels are higher in nursing students in the upper grades and the initial clinical practice affects their stress levels [ 20 , 21 , 22 ]. In order to reduce the stress and its negative effects in nursing students, first of all, to determine their stress levels, their responses to stress, coping strategies and the factors affecting their stress levels is very important.

Research questions

What are the stress levels, bio-psycho-social responses, and coping behavior of nursing students during the practical training?

Which variables affect the stress level of nursing students?

A cross-sectional design was used.

Procedure and samples

The study’s population consisted of 1st, 2nd, 3rd, and 4th-year students [ n  = 1181] of nursing school. A cross-sectional study was conducted between September 2018-May 2019.Since it was aimed to reach the entire population, no sample selection method was used. The inclusion criteria for the study were (1) voluntary acceptance of study participation (2) being during the period of practical training. The number of students was 300 for first grade, 309 for second grade, 285 for third grade and 287 for fourth grade. All of the students [ n  = 996] who meet inclusion criteria are included in the study. The response rate of the questionnaires is 84%. ( n  = 996/1181).

Data was collected during the practical training for each grade. The Faculty of Nursing has an integrated education system. The integrated education system is based on holistic learning. It enables the student to see the big picture instead of learning small parts and subject areas are associated according to a subject. The integrated education programme, which includes a structuring from health to disease, is organised to include basic knowledge, attitudes and skills related to the subjects related to care. In the first, second and third years of the integrated education programme, courses are conducted as modules, active education methods are used, and skills training is provided in laboratories and clinics. The fourth year is organised as an internship programme. Practical training starts to in the second term of the first year in the Faculty of Nursing. 1st-year students have practical training consist of 13 h per week for one month in Primary and Secondary Schools. 2nd and 3rd-year students have practical training in Hospitals and Primary Care. The practical training of 2st-year students in the third semester consists of 24 h per week for one month in dermatology, otolaryngology clinics, eye clinics, etc. In the fourth semester, their practical training includes 24 h per week for two months in İnternal Medicine and Surgery clinics. The practical training of 3rd-year students comprises 24 h per week for three months in pediatrics, obstetrics (fifth semester) clinics and psychiatry clinics, primary care (sixth semester). 4th-year students (internship) are in practical training (eight different nursing fields fundamentals of nursing, internal nursing, surgery nursing, pediatric nursing, obstetric and gynecological nursing, psychiatric nursing, public health nursing) during the seventh and eighth semesters. They have practical training 32 h per week each semester.

Data collection tools

Data was collected using by Socio-Demographic Questionnaire, The Student Nurse Stress Index (SNSI), The Bio-Psycho-Social Response Scale (BPSRS) and Coping Behavior Inventory (CBI) Socio-Demographic Questionnaire consists of seven questions such as age, gender, grade, employment status, smoking status, choosing nursing profession willingly and academic status.

The student nurse stress index (SNSI): SNSI that developed by Jones & Johnstone (1999), consisted of 22 items, and four subscales which include academic load, clinical concerns, personal problems, and interface worries [ 23 ]. SNSI is a five-point Likert-type scale ranging from 1 [not stressful] to 5 [extremely stressful]. The Turkish validity and reliability study was conducted by Sarıkoç, Demiralp, Oksuz, Pazar, [ 24 ]. Its Cronbach α coefficient was 0.86. Turkish version of the scale consists of four subscales as personal problems, clinical concerns, interface worries, and academic load. The higher scores obtained from SNSI indicate the high-stress level.

The bio-psycho-social response scale (BPSRS): The BPSRS, developed by Sheu, Lin, Hwang (2002), consist of 21 items and three subscales about symptoms relating to the students’ physical, psychological and social health [ 25 ]. BPSRS five-point Likert-type scale from 0 to 4. Its Cronbach’s alpha coefficient was 0.90. A higher score indicated the presence of more symptoms and poorer physio-psychosocial status [ 25 ]. The Turkish validity and reliability study was conducted by Karaca et al. [ 26 ]. The Cronbach’s alpha coefficient of the Turkish version was found to be 0.91 [ 26 ].

Coping behavior inventory (CBI): The original version of CBI that developed by Sheu, Lin, Hwang, (2002), consists of 19 items and four subscales as avoidance, problem solving, stay optimistic and transference [ 25 ]. The scale is a five-point Likert-type scale from 0 to 4. Its Cronbach’s alpha coefficient was 0.76. A higher score in one factor indicated more frequent use of this type of coping behavior [ 25 ]. The Turkish validity and reliability study was conducted by Karaca et al. (2015) and its Cronbach’s alpha coefficient was 0.69 [ 26 ].

Data analysis

The data were evaluated using the SPSS 21 (Statistical Package for the Social Sciences). Descriptive statistics was used as mean and standard deviation. One way anova test was used to compare scale scores (SNSI, BPSRS, CBI) according to graders. Multiple regression analysis was used to determine the variables (gender, employment status, smoking status, willingness of the choice of the nursing profession, academic achievement status) affecting stress level. For all effects, we used the standard significance level of α = 0.05.

Ethical considerations

This study was approved by Ege University Scientific Research and Publication Ethics Committee (Approval Number: 56/2018). The participants received information about the research objectives and procedures, and their written permission was obtained by means of informed consent form before data collection.

The mean age of nursing students is 21.32 ± 1.57 years. Of the students, 91.9% are females and 26.5% are freshmen, and 5% are working outside the school (Table  1 ).

When nursing students’ total and subscale SNSI mean scores were compared, a statistically significant difference was found between the mean scores of total SNSI and academic loads, interface worries and clinical concerns subscale (Table  2 ). The first grade nursing students’ mean score of academic load subscale was found to be statistically significantly higher than of second and third graders ( p  < 0.05). The third and fourth grade nursing students’ interface worries subscale scores were also statistically significantly higher than of the first and second graders. In the clinical concerns subscale, the second and fourth grade nursing students had significantly higher clinical anxiety than the other graders and the first-year nursing students had lower clinical concerns than other graders. When the total SNSI mean scores were compared, fourth grade nursing students’ stress level was found to be statistically significantly higher than of other graders, and the first grade nursing students’ stress level was statistically lower than of other graders.

It was established that nursing students have shown emotional symptoms and social-behavioral symptoms the most, whereas physical symptoms were shown the least (Table  3 ). When the total and subscale mean scores of BPSRS were compared according to nursing students’ grades, a statistically significant difference was detected in subscales of total BPSRS, emotional symptoms and social behavioral symptoms. In the emotional symptoms subscale, the first year nursing students had less emotional symptoms than other graders. In the social behavioral symptoms subscale, the mean scores of fourth grade nursing students were found to be significantly higher than of other graders. When total BPSRS mean scores were compared, it was observed that the fourth grade students had more bio-psycho-social behavioral symptoms than the first grade students.

It was found that to cope with stress, nursing students used the strategies transference, staying optimistic, problem-solving and avoidance, respectively (Table  4 ). When nursing students’ behaviors related to coping with stress were evaluated according to grades, no statistically significant difference was found between the subscale scores of avoidance, staying optimistic and transference, whereas only the problem-solving subscale was statistically significant. In the problem-solving subscale, the problem-solving skills have increased significantly as the class increased ( F  = 72.63; p  = 0.00).

The relationship between nursing students’ stress level and gender, willingness to choose nursing profession, smoking status, employment status and academic achievement status was evaluated using regression analysis (Table  5 ). The extent to which nursing students’ stress levels were predicted by variables such as gender ( β =-0.22, p  = 0.00), choosing nursing profession willingly ( β =-0.27, p  = 0.00), smoking status ( β  = 0.28, p  = 0.00), employment status ( β  = 0.14, p  = 0.00) and academic achievement status ( β =-0.34, p  = 0.00) was determined by applying linear multiple regression. As a result of this process was detected as R  = 0.84, R2  = 0.70, and 70% of the total variance on stress level was explained by these variables. The stress level was found significantly higher in female students, working students, smokers, those who did not want to choose the nursing profession and those with low academic achievement.

One of the most important stress factors for nursing students is practical training periods especially an initial period of practical training [ 21 ]. It is stated that nursing students experience more stress in clinical practice periods than other periods [ 16 , 21 ]. In the literature, studies investigating the effects of grade on the stress level of nursing students have shown mixed results. Eswi, Radi, Youssri reported that there was no relationship between grade and stress level [ 27 ]. In a study conducted by Shaban, Khater, Akhu-Zaheya, it was found that nursing students were more sensitive to stress due to reasons such as transition to university life, managing their own needs and gaining new social skills, especially during the first years of education. In this study, unlike other studies, the first-year nursing students’ stress level was found lower than of other graders [ 6 ]. Aedh, Elfaki & Mohamed, reported that nursing students who are in the second year of nursing education have experienced higher level of stress than other grades [ 28 ]. In this study, although the second grade was not the highest stress level group, the stress level showed a rapid increase compared to the first grade and the clinical concerns subscale scores were found higher than other grades. Third and fourth grade nursing students’ mean interface worries scores were found high the other grades. Several studies have similarly reported that, nursing students’ stress level was found higher in the last period of nursing education compared to other periods [ 15 , 22 ]. In a qualitative study conducted by Admi et al. (2018) it was found that conflict between professional beliefs and the reality of hospital practice were stressors for final year students [ 19 ]. In the study conducted by Bhat (2021) et al. it was reported that training on invasive procedures (safe catheter etc.) should be standardised in undergraduate education and this should be made part of the annual or biannual compulsory training for healthcare personnel [ 29 ]. Similarly, in this study, the stress level of fourth-grade nursing students was found higher than of other graders, and fourth-grade nursing students’ mean scores of clinical concerns and interface worries were higher than of other graders. The results of our study indicate that the first-grade nursing students had problems adapting to the intensive pace of nursing education and that they experienced stress; accordingly, second-grade nursing students who first-time took to practical training and fourth-grade nursing students who had the longest practical training period also experienced stress due to practical training.

In several studies found that nursing students experienced higher levels of stress, physical and psychological symptoms than the students in other health disciplines [ 6 , 30 ]. Chen & Hung reported that nursing students demonstrated physical symptoms toward stress mostly, and social-behavioral symptoms the least [ 8 ]. In the study carried out by Kassem & Abdou, when the bio-psycho-social responses experienced by nursing students were evaluated, it was found that emotional symptoms were the most common and social-behavioral symptoms were the least [ 11 ]. In another study conducted by Durmuş & Gerçek with nursing students, it that bio-psycho-social responses were found to be occurred mostly in fourth grade students [ 31 ]. In all classes, the most often emotional symptoms were observed in nursing students followed by social behavior symptoms and physical symptoms respectively [ 31 ]. The present study showed that nursing students demonstrated emotional symptoms and social-behavioral symptoms the most, whereas physical symptoms were demonstrated the least, and these results were consistent with results from most of previous similar studies. It was found that fourth-grade nursing students experienced more Bio-Psycho-Social Responses than freshmen and emotional symptoms were higher in second, third and fourth grade nursing students and social behavioral symptoms were higher in fourth-grade nursing students. This difference may be explained by the fact that because fourth-grade nursing students’ stress levels were higher than of other graders, they showed more Bio-Psycho-Social Responses.

Durmuş & Gerçek found that first, and the third-year nursing students have usually used strategies for coping with stress such as stay optimistic and avoidance, respectively [ 31 ]. Also, the same study showed that second and fourth-year nursing students have used problem-solving most [ 31 ]. Many studies found that nursing students have generally used problem solving as a coping strategy [ 5 , 8 , 11 , 19 , 32 ] and the avoidance at least [ 5 , 6 , 8 ]. Sheu, Lin, Hwang reported that using effective ways of coping with the problem will facilitate returning to stable status by allowing reduction of negative consequences of stress [ 25 ]. The present study showed that nursing students most often used transference and least avoidance strategies to cope with stress, and as the students’ grade levels increased, also the level of using problem-solving skills increased. This situation indicates that the problem-solving competencies involving in nursing education are being provided to the students. The fourth grade of nursing students who has highest practical-training hours possess problem-solving skills more than other grades because of the positive effects of the practical applications encountered in a large number of complicated situations on the problem-solving skills of the nursing students.

In the present study, when the interaction between nursing students’ stress level and gender, working status, smoking status, willingness to choose nursing profession and academic achievement status was evaluated, it was found that female students, employees, smokers, those that have chosen nursing profession unwillingly, and those with low academic achievement had significantly higher stress levels. It was reported in different studies that academic success [11,20,], gender [ 20 , 21 , 33 ] have affected students’ stress levels and also their working hours outside of nursing education have affected their stress level [ 11 ]. Although it is important for all students to reduce stressors and to provide support for the use of coping mechanisms; especially female students, employees, smokers, those that choose the nursing profession unwillingly, and those with poor academic achievement should be supported more.

Limitations

This study has some limitations. Unlike other nursing schools in our country, this research was carried out in a nursing school where an integrated education system was applied. The findings could be specific to this college of nursing. Therefore, the generalizability of results may be limited. Besides, the small number of male students is another limitation of the study. SNSI, BPSRS, and CBI are a self-reported questionnaire. This can lead to social desirability bias in respondents.

Reccommendations

It is recommended that long-term studies be conducted to understand the long-term effects of stress experienced during nursing education and to develop sustainable support mechanisms. Support mechanisms may decrease stress levels and their negative effects on nursing students and can promote nursing students’ well-being and academic success, especially during practical training. Exploring what is nursing students of stress levels and coping strategies during education, can inform post-graduation preventive strategies. Also, evaluating the current stress levels and coping strategies in different nursing education programs is crucial for identifying gaps and areas for improvement. Interventional and qualitative studies are crucial to providing concrete recommendations for educational institutions and policymakers to address stress among nursing students.

According to results of the present study, the stress levels of fourth-grade nursing students were higher than of other graders and causes of stress varied as regards grades. The higher level of stress in the senior nursing students that have the maximum responsibilities and stay times of practical training and the bio-psycho-social responses given by students associated depending on this stress indicate that those clinical practices are one of the main sources of stress for nursing students. Due to the nature of nursing education and nursing practices, students use their problem solving skills as a coping strategy. However, the presence of stress-related emotional and social-behavioral symptoms in nursing students indicates that they cannot cope with stress sufficiently. Internship, which is the preparation period for the transition to professional life for nursing, is the period in which nursing students experience the most stress. Students’ learning to cope with stress in this period will enable them to use these strategies in their professional lives. Nursing schools can consider this period as an opportunity period to reduce and cope with stress, which is one of the important risk factors for nurses.

To develop stress management and the stress-coping mechanism of nursing students, it was recommended that courses or counseling units should be available, nursing educators should support students in the clinical areas, receive regular feedback from the students about practical training, and cooperate with clinical nurses to increase nursing students’ clinical compliance. Also, in particular, female students, working students, smokers, those that have chosen nursing profession unwillingly, and those with low academic achievement should be encouraged to receive individualized or group support for stress management and in coping with stress.

Data availability

The data that support the findings of this study are not openly available due to reasons of sensitivity and are available from the corresponding author upon reasonable request.

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M. S Conception and design, data acquisition, data analysis and interpretation, writing, give final approvals. S. T Conception and design, data acquisition, data analysis, writing, give final approvals. Z. E. S Data acquisition, data interpretation, give final approvals. S. D Data acquisition, data analysis, give final approvals. F. S Conception, writing, give final approvals.

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Solak, M., Topçu, S., Sert, Z.E. et al. Evaluation of stress, bio-psycho-social response and coping strategies during the practical training in nursing students: a cross sectional study. BMC Nurs 23 , 610 (2024). https://doi.org/10.1186/s12912-024-02265-5

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Received : 30 June 2024

Accepted : 14 August 2024

Published : 02 September 2024

DOI : https://doi.org/10.1186/s12912-024-02265-5

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