The Future of Code Optimization

Software development services are the main muscles behind all mobile applications, desktop programs, and online platforms we use on a daily basis.

In simple terms, software creation is the process of preparing, building, releasing, and supporting software or all the movements between the software’s birth and final presentation.

These steps are planned and put into a specific hierarchy, known as the software development life cycle (SDLC).

As an inherent part of the IT market, SDLC has witnessed rapid metamorphosis recently, largely influenced by the penetration of AI.

Why Many Developers Turn to AI in the Context of Software Development

According to the survey taken in 2024, over 80% of software engineers noted that the most meaningful benefit of using AI, LLMs, and ML in their workflow was the better state and quality of software produced.

Moreover, about 60% of interviewees highlighted that AI sped up learning and helped achieve a higher coefficient of productivity.

Also, an important factor was the variety of tools that can be used at all stages of SDLC.

Among all the tools used in AI-based software development, programmers most often mentioned ChatGPT, GitHub Copilot, Google Gemini, Bing AI, and Visual Studio Intellicode.

Areas Where AI Can Improve Software Development Processes

What comes first to your mind if asked about the most popular AI applications in software engineering? Definitely code creation. And that’s true.

Approximately 82% of programmers admitted they were using AI tools for writing code. Digging for explanations and solutions was the second most prevalent use for AI with around 68% of tech specialists. But what about other stages?

Requirement Grouping and Breakdown

First things first, AI can help turn imprecise requirements into a clear, realistic plan by examining competitors, customer feedback, user desires, and market directions.

Using natural language processing (NLP), AI tools can break down user stories and business needs into specific technical prerequisites that, in turn, can make it easier for development teams to get what’s expected.

Prototyping, Probing, and Making Preliminary Projects

Prototyping is paramount for visualizing ideas and quickly testing the market. With just a few inputs or sketches, AI tools can instantly produce basic wireframes and mockups and help developers assemble prototypes and MVPs that founders/investors/first users can review and give feedback on.

By automating this early design stage, AI saves time and lets teams abrade designs before activating full development.

AI-Assisted Coding

AI coding tools can literally in a split second create code snippets, basic code templates, and even complex algorithms based on simple text prompts.

GitHub Copilot, for instance, can generate reusable code and reduce repetitive tasks, as well as suggest practical code ideas along the way.

In general, at this stage, AI can take on all the same type of effort-consuming tasks, while developers can spend more time and creativity on the fine and painstaking work of solving more complex problems.

Code Refactoring and Amendments

Code refactoring refers to code reorganization without amending what it actually performs. AI-powered refactoring tools scan the code, pinpoint the spot that could be refined, and suggest ways to rearrange it without changing the whole structure.

On top of that, artificial intelligence tools can keep their code clean, build up no technical debt, and make it last longer by sticking to good coding practices.

Automated Testing, QA, and Troubleshooting

AI-powered testing tools can independently create tests, run them, and even prioritize which parts to check first based on past problems.

With AI, testing engineers can cover regression (checking old features still work), load testing (seeing how much traffic the software can endure), and even exploratory testing (trying to search out unexpected gaps).

Machine learning, in its turn, can spot parts of the code that are more likely to have defects, speed up quality checks, and make the software more well-run.

Debugging and Code Review

As any engineer will tell you, debugging can be a time-intensive process. Thanks to AI tools, it’s now possible to visibly speed up this phase, quickly spot bugs, and even get recommendations for fixes.

By catching issues early, these tools add to code quality, save time on debugging, and help teams adhere to good coding practices, which means fewer bugs when presenting software and less work on future updates.

Project Coordination and Operations Administration

Developing software for a particularly complex product involving collaboration between 2+ departments requires not only premier know-how but also precise coordination. And while many are hesitant about this point, AI has already proven its rationality in arranging tasks, prognosing deadlines, and regulating progress.

With AI, it has also become possible to balance workloads, assign tasks based on who is available and qualified for each job, and generate progress reports. And the icing on top: AI can analyze project data to identify potential slowdowns and make judgments that improve output.

Possible Pitfalls and Difficulties of Using AI in Software Development

Don’t blindly invest in artificial intelligence development services just because they’re hot right now. AI is an undeniably sensible solution, but it can make mistakes—especially with complex projects that need detailed, human insight.

AI-generated code might miss the context that a human would catch, which means developers often still need to step in to double-check the outcomes.

Most AI tools also need access to sensitive data or code, which can be risky. Even Samsung once encountered leaks, and there’s always a probability of exposing information or creating security vulnerabilities.

Also, no one can avoid substantial expenses. AI tools can be expensive to set up and maintain with the need for training, updates, and sometimes even new hardware. Just picture what this might mean for smaller teams.

And last but not least. Many AI systems operate similarly to black holes, where they make suggestions but don’t explain how they arrived at them. The lack of clarity can make developers hesitant to trust the recommendations, especially when they want to understand the logic behind a particular decision.

How to Use AI in Software Development: Practical Proposals

If the previous point hasn’t stopped you and your team is looking to get started with AI, here are some tips to help you pull through.

First, think about where AI could make an impact: testing, debugging, project management, and so on. When you know where AI can add sense, pick the right tools. Look for ones that fit your team’s needs, budget, and current skills. Otherwise, you can build your own at SCAND.

If you for now have no budget for full AI-powered development, start small with a pilot project. Trying out AI tools on a smaller scale lets you see how well they work and get feedback without fully committing right away.

The next thing is teaching. AI tools are only as useful as the people using them. A bit of training goes a long way in helping your team feel comfortable with the tools and understand how to make the most of them.

Lastly, regularly inspect how the tools are working and gather team opinions. Small alterations here and there can polish the process so you’re getting the best results.