Ben and Ryan are joined by Bill Harding, CEO of GitClear, for a discussion of AI-generated code quality and its impact on productivity. GitClear’s research has highlighted the fact that while AI can suggest valid code, it can’t necessarily reuse and modify existing code—a recipe for long-term challenges in maintainability and test coverage if devs are too dependent on AI code-gen tools.
GitClear is a developer-friendly code review tool that aims to deliver higher developer satisfaction and faster releases. Check out their blog or find them on GitHub.
GitClear’s research focuses on how AI code-gen tools have impacted code quality (and not in a good way).
Find Bill on LinkedIn.
Chapters
00:00 Introduction
00:30 Background of the Research
06:09 Business Model of GitClear
09:46 Copy Pasted Code
10:26 Churn Code
12:21 Code Readability
14:12 Code Suggestions and Auto-Completion
16:34 Drop in Moved Code
23:18 Larger Token Windows
26:31 Improving Gen AI
28:46 Conclusion
TRANSCRIPT