How will AI affect software development?

To warm up, we checked if ChatGPT could pass our tech interview. We asked theoretical and practical questions designed for senior engineers and found that it answered them perfectly, albeit more verbose than expected.

ChatGPT also completed one of our coding challenges spectacularly, documented the iterations it created, and came up with good variable naming (which we all know is the biggest challenge in coding).

However, the unit tests we asked for had mixed results, requiring further human intervention and prompt refinement: In one of the more advanced tasks, we had to explicitly tell ChatGPT to include a critical step it omitted, simplify the code, and optimize performance issues.

The most complicated test assignment resulted in buggy code; after requesting ChatGPT to fix it, it hallucinated and generated new bugs instead of fixing the original one. The test was still useful, though, because refactoring the faulty solution was much quicker than writing it from scratch.

The final challenge was feature development. Our prompt was straightforward: Create a JavaScript command line script that calls the Instagram API of a user and stores the data in a database table.

ChatGPT provided detailed answers, describing a robust database model and anticipating potential pitfalls.

Our ongoing collaboration with the AI resulted in greater efficiency, particularly when it comes to creating tests and repurposing boilerplate code.

The prompt affects the quality of the generated output: ChatGPT is probabilistic, meaning prompt iterations produce distinct results, and the more detailed the instructions, the better.

We’ve also learned that current AI tools are inherently vulnerable–to hallucinations, lack of context, and poor training data. Since the code quality decreases as the complexity rises, knowing what you need is critical, and reviewing the output to ensure it’s free of bugs, performance issues, or overly complicated code is mandatory.

Here are a few things you can already use AI for today:

• Add unit tests to a project
• Explain and audit legacy code
• Document new and existing codebases
• Create quick MVPs

Software engineering will undoubtedly metamorphose as AI evolves–and it does. Fast. Professional programmers would move from writing code to reviewing, optimizing, and maintaining it.

AI will become instrumental in the development workflow, and tools that are well-integrated with IDEs will grow more popular, helping developers generate tests, document and explain code, and save time wasted searching external sources like Google or Stack Overflow.

Software developers who will take the time to adopt these new capabilities and adapt to this new paradigm will gain an unexpected benefit: As coding gets easier and more accessible, programmers could spend less time in their IDE and shift their focus to developing other indispensable skills, like communication, empathy, and teamwork.

Do you want to get in touch with the expert of Project A? Then join them on October 10 and 11 for Tech Day and PAKCon, where we dive deep into the future of AI and other exciting topics.