How artificial intelligence transforms the software development process: From prompt to code
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Just five to seven years ago, the use of artificial intelligence in software development was more of a fantasy than a working concept. But today it is routine—AI-based assistants and tools are available to literally every developer. The world of IT has changed forever.
Today, the use of artificial intelligence in programming opens up many revolutionary opportunities for developers, from automation of routine tasks to generation of high-quality code and quality control. And AI can be used by both a global software development company and a small local start-up.
In this article, we will look at key AI tools that are already changing approaches to coding, testing, and debugging software today.
What are AI tools for developers?
We are talking about a wide range of solutions based on smart algorithms that can perform certain tasks in the software creation process. Let's break down the main tools for developers using AI, categorising them by category.
▶ Code generation and augmentation tools. These tools use machine learning models to anticipate and generate code based on context, comments, or already existing code. They act as smart assistants to help developers write code faster and more efficiently. The most popular solutions in this category are GitHub Copilot, Tabnine, Codeium, Replit, Amazon CodeWhisperer, etc.
▶ Testing and debugging tools. Such solutions use AI to automate code testing and debugging processes. They can generate unit tests, identify potential bugs and vulnerabilities, and suggest automatic fixes. Typical examples of such tools are Diffblue Cover, Applitools, DeepSource, AskCodi, Snyk, and others.
▶ Code analysis and improvement tools. Such tools use AI to analyze code for compliance with the best coding practices and standards. They can detect code duplicates, performance issues, and other factors affecting product quality. Popular solutions in this area include Codiga, SonarQube, DeepCode, etc.
▶ Documentation tools. This category covers solutions that use AI to automate, improve, and simplify the process of creating/maintaining software documentation. Even basic ChatGPT can greatly simplify document preparation and project maintenance. There are also specialized development solutions such as Mintlify and Scribe.
▶ Project management tools. Project management tools are a separate class of software that automates processes like reporting, scheduling, and progress monitoring using artificial intelligence. They may assist with risk identification, resource optimization, and sprint timetable prediction.
AI tools for coding automation
Undoubtedly, AI tools for automating code writing are becoming more and more important in every developer's work. A lucky hint from a chatbot or an accurate auto-completion can save a programmer several hours of working time or at least nudge him in the direction of the right decisions. How does AI help to create code? Let's name the main ways.
▶ Auto-completion. AI analyses the code context and suggests the completion of lines, functions, or even whole code blocks. This greatly accelerates the coding process and reduces typos.
▶ Code generation by description. A developer can describe the intended purpose of the code using natural language, and the AI will generate it quite efficiently. This is especially useful for rapid prototyping or routine tasks.
▶ Code recommendations. AI can analyse developer code and suggest improvements; it can also identify potential bugs or vulnerabilities. This capability helps maintain high code quality and adhere to programming best practices.
▶ Code conversion. AI may be utilized for refactoring and code translation across various programming languages.
Popular automated coding tools include GitHub Copilot, Tabnine, Codeium, Replit, and Amazon CodeWhisperer.
AI for software testing
Concurrent with this is AI simplifying quality control; hence, it alters methods of software testing. AI frees testers' time for more difficult jobs by helping to automate mundane chores such as creating test cases and result analysis.
By automatically adjusting test scripts to fit changes in code or interface, artificial intelligence helps to keep them current and lessens the need for human involvement. In addition, algorithms can predict potential bugs based on analyzing historical data, allowing testers to focus on critical areas.
Popular AI-based testing tools include Diffblue Cover, Applitools, Testim, and Zaptest.
AI and software performance optimisation
Inefficient code can affect software performance and usability. Outdated code bases or suboptimal algorithms can slow down software performance, especially as the amount of data it works with grows. To overcome this problem, developers resort to refactoring (optimizing and cleaning) the code base.
Today, artificial intelligence for code improvement allows for radically simplifying or even automating refactoring in several directions:
▶ Code analysis. AI can automatically analyse code, detect problems, and give recommendations on optimization. This process includes detecting "dead code," complex simplification sections, and potential errors.
▶ Performance evaluation. Some tools can analyse code performance and suggest improvements to increase application efficiency and speed.
▶ Automatic refactoring. Certain artificial intelligence-based technologies may automatically perform refactoring tasks, such as renaming variables and breaking apart big functions into smaller ones, eliminating code duplication, etc. This method reduces the time needed to do regular chores.
▶ Testing and verification. AI can automatically generate test scenarios to verify code correctness after refactoring, which helps to avoid new bugs.
▶ Hints and suggestions. AI can provide hints and suggestions, helping developers write cleaner and more efficient code from the start.
Several AI-based tools are available to developers for code refactoring: Codota (Tabnine), Sourcery, DeepCode, and IntelliSense.
Conclusion
Technology is developing too fast. Therefore, it is impossible to predict the future; any idea has the potential to take off. And if you have an idea for an IT product that can change the future of your business, or you want to discuss a specific development request—don't waste time; seek professional advice right now.
