Generative AI is no longer just a futuristic concept in software development; it’s actively shaping the industry. With tools like GitHub Copilot, Vercel’s v0, and Cursor, AI is becoming a regular companion in the coding world, helping developers write new code and maintain existing systems. However, the day-to-day responsibilities of developers extend far beyond new development. A significant chunk of their work revolves around refactoring and maintaining legacy code. So, how does the process of refactoring look when the code you’re working with wasn’t written by a human but by an AI tool?
As AI tools become more integrated into development workflows, it’s crucial to understand their role in code maintenance. Can AI-generated code truly lighten the load for developers, or does it introduce new challenges? Real-world developers weigh in on their experiences with AI-written code, shedding light on the unexpected hurdles and sometimes surprising benefits that come with refactoring AI-generated work. While AI tools are a powerful asset, their effectiveness in the long run hinges on how they fit into the broader process of code maintenance.
Despite the buzz surrounding generative AI, developers agree that AI-written code can be both useful and weird. Many developers express enthusiasm for the potential of AI to assist with coding, but they also recognize its quirks. For example, the code generated by AI often lacks the consistency that comes from human developers adhering to specific style guidelines or naming conventions. This inconsistency can lead to a fragmented codebase that requires significant cleanup to make it more manageable and maintainable. While the AI’s syntactical accuracy is often impressive, it’s the subtle issues—like unclear variable names and overly complex logic—that developers must fix.
The peculiarities of AI-generated code go beyond just cleaning up variable names and improving readability. Some developers, like Travis Rehl, CTO at Innovative Solutions, point out that AI sometimes introduces unfamiliar patterns or libraries that may not align with a project’s usual architecture. Refactoring these patterns without understanding the AI’s reasoning can be challenging. There’s also the risk of inadvertently breaking dependencies that the AI has built into the code. As Rehl describes, working with AI-generated code can feel like navigating an environment that’s both familiar and alien, filled with choices that sometimes leave developers questioning the AI’s logic.