AI-Assisted Software Development: How AI Tools Help in the Software Development Lifecycle

Like any other modern industry, software development can now hardly be imagined without artificial intelligence. The more this technology grows, the more tools appear to make the work of a software developer easier. For each stage of development, there are already many platforms, applications, and extensions that developers (and not only they) actively use. About 75%, to be precise.

What exactly are these tools? How exactly do they affect software development? Do they have any disadvantages? We will tell you about this in today's article. Plus, you can get a more detailed description of AI in development here thanks to our reports! Read on to learn more.

Understanding AI-assisted software development

In short, AI-assisted software development means the use of artificial intelligence tools/techniques to help developers write, test, and update code more efficiently. It doesn’t replace developers but acts more like an assistant that boosts productivity, reduces repetitive work, and catches issues faster.

Such an approach to development becomes more popular day by day. And the numbers speak for themselves:

• 78% of organizations reported using AI in at least one business function in 2024.

• 83% of companies reported using AI in their business strategies as one of their top priorities.

• A randomized controlled trial at Google found that AI assistance decreased development time by approximately 21% on complex enterprise tasks.

• A study evaluating GitHub Copilot reported up to 50% time saved in code documentation and autocompletion and 30–40% in repetitive coding tasks, unit test generation, debugging, and pair programming.

• 82% of developers used genAI for at least two phases of the development process in 2024, and 26% incorporated it across four or more stages.

AI-assisted development can look like AI copilots, genAI for code snippets, automated testing, and code review assistance. We’ll talk about it in more detail.

The value of AI-powered SDLC

Whether we are talking about software development in general or discussing each stage separately, there’s no doubt that AI has a lot to offer. Here are some of the most noticeable benefits:

• Faster development: AI tools can suggest entire lines or blocks of code to reduce the overall typing and searching time. This can be extremely helpful during prototype and MVP development. Also, if you need to add people to your team, new developers can understand codebases faster with AI-generated explanations.

• Smarter decision-making: Modern AI models are fed large code bases and can provide more efficient code patterns. They can also study historical data to provide better estimations.

• Better developer productivity: Since AI can easily handle repetitive work, developers have more free time to put effort into higher-level and more business-relevant tasks.

• Consistent quality of the final code: AI can notice inconsistencies and recommend strict standards for better maintainability.

• Data-driven optimization: Since performance bottlenecks can become a real headache, you can ask AI to suggest improvements for your software’s architecture if necessary. 

AI in different stages of SDLC

Now, let’s move to the main part: How exactly does AI help software developers? Let’s dive into the details of the ways your dev team can use artificial intelligence to provide you with the best results.

AI in coding

There are plenty of ways AI can help during coding. AI-based platforms (GitHub Copilot, Tabnine, Codeium) can suggest the next lines of code as developers type. These tools are trained on huge code repositories so they can provide context-aware suggestions with the best coding practices in mind.

Large language models (LLMs) can also come in handy. Models like OpenAI's GPT-4.1 can translate plain text prompts into functioning code. Developers can describe features they want to implement in plain English and then the AI converts them into code.

AI is also valuable during refactoring. AI-based tools look through the existing code to see what can be improved (redundant code, performance bottlenecks, bugs). Such an approach makes maintaining clean code way easier.

Another extremely useful hack that often gets missed out is code translation between languages. AI models will effortlessly translate code from one programming language to another, so platform migrations or new integrations will be smoother. It can be especially useful for legacy systems and their updates.

AI in UI/UX design

AI can be incorporated into all parts of the UI/UX design process. At the beginning, ChatGPT will be your best option for preparing a comprehensive roadmap and outlining the whole project journey. Then, tools like FigJam AI can create user flows to visualize how users will navigate the platform.

Recraft AI and/or Figma AI will speed up the creation of wireframes and mockups so that designers can quickly explore the possible layout options and see what will work best. The same tools can also recommend fonts and color schemes to match the business needs and better the visual experience.

Further into the design process, AI can help with creating content and illustrations for more realistic visualisation. Gemini and Leonardo can generate custom illustrations and icons to support visual consistency. Gemini can even match the colors to mood boards created by designers. And various LLMs will refine the text content and write clear microcopies like error messages or instructional text that meet users’ needs.

AI in QA

Artificial intelligence comes in handy during all stages of quality assurance. For example, in requirements analysis, AI is mostly used to extract key information and summarize complex technical documents so QA teams can understand the core requirements.

AI can optimize the test planning stage by predicting which test cases are more likely to fail based on historical testing data. AI-based tools like qTest and Test.ai use data analytics and predictive modeling to identify the highest-priority test cases so the QA team can focus on the most urgent and important tasks.

Also, AI can assess the app’s requirements or code itself to generate initial test cases. Tools like Mabl automatically generate functional and regression test cases to provide the QA team with a strong starting point for test coverage.

AI plays a key role in test execution, especially in automated testing. For example, Applitools Eyes can conduct visual regression testing by comparing screenshots of different versions of an application. Also, AI can optimize automated test execution by selecting the most relevant tests based on changes in the code.

Finally, with the help of AI, your QA team can generate reports from test results and highlight what can/should be improved. For example, Qtest Insights can automatically generate actionable insights from test results, which helps teams identify high-priority issues.

More use cases of AI in software development

Beyond coding, UI/UX design, and testing, AI can be used in other parts of the SDLC. Here are key use cases:

• Requirements gathering: With the help of Natural Language Processing (NLP), you can analyze stakeholder conversations, emails, or documents to identify relevant requirements automatically.

• Effort and time estimation: AI can estimate the time and resources necessary to complete the development. It can also assess team availability and skill sets to suggest the best possible team compositions.

• Smart maintenance: AI detects early warning signs of software degradation or component failure, alerts the team, and suggests the necessary measures to fix the code.

• ROI forecasting: Product managers can use AI to see which features will bring the most value based on what users do inside the app.

• Feedback review: NLP helps group and sum up user feedback (from support tickets, reviews, emails, chats, comments, and more) to highlight the most urgent issues or feature requests.

How to integrate AI into your SDLC?

AI integration doesn’t mean you can just throw in some tools and start making progress. It’s more about rethinking workflows to make them more data-driven. Here's a step-by-step approach to doing it right:

1. Assess your opportunities

You should start by identifying the most suitable stage for AI entry. Is that planning? Coding? QA? Depending on the stage you choose, your toolset and training strategies will look a bit different. Also, it’s better not to try to AI-everything at once. Start with the most repetitive or time-consuming tasks.

2. Choose the right tools

At this stage, you should define what tools/platforms fit naturally into your existing workflows. For example:

• Planning: Jira plugins with AI

• Coding: GitHub Copilot, Tabnine, Amazon CodeWhisperer

• Testing: Testim, Mabl, Functionize

• Monitoring: Datadog, Dynatrace with AI root cause analysis

Look for tools with well-built APIs/plugin compatibility and a strong community so you won’t have trouble while integrating the tools.

3. Embed AI into DevOps pipelines

AI works best when it’s automated and always-on, so your best option is to include these tools in your CI/CD pipeline. You can do it by including AI test generation in your build process or adding AI anomaly detection post-deployment. This turns AI into a proactive helper and not another tool your team has to remember to use.

4. Prepare your data

Any AI thrives on data. Your AI-based software development tools will, too. You need to make sure you’re collecting structured logs, you have access to historical project data, and your security measures are top-notch.

5. Train your team

Not all developers are eager to incorporate AI into their work. And even the best tools will flop if your team isn’t on board. You need to provide training sessions/documentation on using AI tools to reduce your team’s anxiety and fear of the unknown. Also, create clear guidelines on when/how to trust AI output and where to override it.

6. Iterate and improve

Now, when everything is up and running, you need to monitor how exactly your team reacts to AI. You can set KPIs for your AI tools (like % reduction in bug rate or time saved on test creation) and collect feedback from your devs. When you get enough feedback, adjust configurations over time depending on your team’s size and needs.

Possible issues, risks, and limitations

Unfortunately, AI is still not almighty. There are a lot of risks and limitations that you can face during the integration process. You need to be aware of them so that you can face them fully equipped.

• AI struggles with context understanding and relies heavily on quality data.

AI tools are highly reliant on the quality of the data they’re trained on. When working with less-than-optimal datasets, AI models often fail to meet project expectations. In some cases, the AI’s lack of fresh and clean data leads to lower accuracy, especially when dealing with real-time inputs like noise or accents.

• Generated code often doesn't fit well with the existing codebase.

AI-generated code snippets can sometimes lack context awareness. While AI could assist in generating pieces of code, it often fails to integrate well with the rest of the codebase. This can result in the need for manual adjustments, negating some of the expected efficiency gains. Additionally, when dealing with legacy projects, AI can struggle to offer optimal solutions.

• Data privacy concerns prevent sharing sensitive code.

A major limitation for any AI usage is data privacy. It prevents developers from sharing large portions of source code with AI systems. The fear of exposing sensitive information restricts the number of ways AI could be utilized in certain projects. This limitation significantly impacts the broader use of AI tools.

• AI sometimes suggests non-existent libraries or methods.

AI sometimes generates inaccurate or even totally made-up results. This can become particularly problematic when the AI invents libraries or methods that don’t exist, leading to ineffective solutions that require manual correction.

To conclude

The growth of artificial intelligence is unstoppable. With time, its influence on all areas of our lives will only become bigger. And since software development is one of the industries closely related to AI, its implementation here is inevitable. As a business, your task right now is to correctly implement AI into your workflows and get your competitive advantage. And with our guide, you can do it pain-free.