Maintaining AI-Generated Code: The Next Phase of the AI Revolution

Taylor Brooks

Taylor Brooks

December 02, 2024 · 4 min read
Maintaining AI-Generated Code: The Next Phase of the AI Revolution

The increasing use of generative AI in software development has brought about a new era of coding, where machines are not only assisting developers but also writing code from scratch. However, as AI-generated code becomes more widespread, developers are now facing a new challenge: maintaining and refactoring code written by machines. In this next phase of the AI revolution, developers are discovering that working with AI-generated code requires a unique set of skills and approaches.

According to developers who have worked with AI-generated code, one of the primary challenges is the lack of consistency in style and naming conventions, making it difficult to maintain and refactor. "The code often lacks consistency in style and naming conventions, which can make a codebase feel disjointed," said Dev Nag, CEO of QueryPal, a software company focused on AI-powered ticket generation. "I've spent many hours cleaning up and standardizing AI-generated code to fit a project's conventions."

Another issue is that AI-generated code can be overly complex, with unnecessary error handling and edge cases. "AI tools are known to overengineer solutions so that the code produced is bulkier than it really should be for simple tasks," said Dhaval Gajjar, CEO of Pranshtech Solutions and CTO of Textdrip. "There are often extraneous steps that developers have to trim off, or a simplified structure must be achieved for efficiency and maintainability."

Despite these challenges, developers believe that AI-generated code has its place within the software development lifecycle. In fact, AI tools can be helpful in the code maintenance and refactoring process. "AI can quickly analyze large codebases and identify areas that need refactoring, potential bugs, or optimization opportunities," said Travis Rehl, CTO at Innovative Solutions. "For simpler refactoring tasks, like renaming variables or extracting methods, AI tools can perform these operations across the entire codebase with high accuracy."

Developers are also finding that AI tools can be used to overcome some of the flaws found in AI code to begin with. For instance, AI tools can be deployed to suggest improvements that might not be immediately obvious to the human eye. "Tools like GitHub Copilot propose code simplification, correction of inefficiency, or even the restructuring of logic once identified from some patterns," said Gajjar.

However, developers emphasize that human oversight is still essential in the code maintenance and refactoring process. "Always review and refine AI-generated code changes," said Jason Wingate, a developer and CEO of Emerald Ocean Ltd. "You may put 'using Sarah's coding standards'—which means absolutely nothing—and it still may say 'Sure! I'll use Sarah's coding standards!'"

Rehl doesn't see this sort of human supervision as temporary, either. "I believe you truly need a human-in-the-loop experience for a refactoring process," he said. "The business context of why the system is designed the way it is may get lost on an AI model and as a result, the human will need to steer it."

As the use of AI-generated code continues to grow, developers will need to adapt to this new reality. "Overall, while AI coding tools have certainly increased our productivity in many areas, they've also introduced new challenges in code consistency and maintenance," said Nag. "They're not the magic bullet that some had hoped for, but rather a powerful tool that, when used judiciously, can significantly enhance a developer's capabilities. The key is finding the right balance and always maintaining that human touch in your codebase."

In the end, the future of AI-generated code maintenance and refactoring will likely involve a collaborative approach between humans and machines. As Rehl put it, "I think that's going to happen over time because of AI. As the AI is exposed to existing systems as a copilot, it's going to start automatically documenting what's surrounding the tree. And then, a year later, it will have enough commentary to understand the business context it was trying to achieve, and it can just take over."

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