Code development – The Role of AI in DevOps

This area is where we see the most significant impact of generative AI and other AI technologies. AI revolutionizes code development by automating tasks such as code generation, bug detection, optimization, and testing. Through autocomplete suggestions, bug detection algorithms, and predictive analytics, AI accelerates coding, enhances code quality, and ensures better performance while aiding in documentation and code security analysis. Its role spans from assisting in writing code to predicting issues, ultimately streamlining the software development life cycle, and empowering developers to create more efficient, reliable, and secure applications.

Many tools employ AI in code development, and one of the most popular tools in this area is GitHub Copilot.

GitHub Copilot is a collaborative effort between GitHub and OpenAI, introducing a code completion feature that utilizes OpenAI’s Codex. Codex, trained on vast code repositories from GitHub, quickly generates code based on the current file’s content and cursor location. Compatible with popular code editors such as Visual Studio Code, Visual Studio , Neovim, and JetBrains IDEs, Copilot supports languages such as Python, JavaScript, TypeScript, Ruby, and Go.

Praised by GitHub and users alike, Copilot generates entire code lines, functions, tests, and documentation. Its functionality relied on the context provided and the extensive code contributions by developers on GitHub, regardless of their software license. Dubbed the world’s first AI pair programmer by Microsoft, it is a paid tool and charges a subscription fee of $10 per month or $100 per year per user after a 60-day trial period.

With Copilot, you can start by writing comments on what you intend to do, and it will generate the required code for you. This speeds up development many times, and most of the time, you just need to review and test your code to see whether it does what you intend it to do. A great power indeed! It can optimize existing code and provide feedback by generating code snippets. It can also scan your code for security vulnerabilities and suggest alternative approaches.

If you don’t want to pay that $10, you can also look at free alternatives such as Tabnine, Captain Stack, GPT-Code Clippy, Second Mate, and Intellicode. Paid alternatives include Amazon’s Code Whisperer and Google’s ML-enhanced code completion.

AI tools not only help enhance the development workflow but also help in software testing and quality assurance. Let’s look at that next.