We’ve been using AI assistants to speed up our sprints, but I’ve noticed a massive spike in "shadow technical debt." The AI writes code that works initially but lacks long-term maintainability or follows outdated patterns. How are you adjusting your Definition of Done (DoD) to ensure AI-generated code doesn't become a nightmare for the next dev who has to touch it?
3 answers
This is a huge issue that many teams overlook in the "honeymoon phase" of AI adoption. In our Scrum team, we updated our Definition of Done to include a specific "AI Verification" step. This means a human must not only check if the code works but specifically look for "hallucinated" libraries or inefficient logic that AI tends to favor. We also started a weekly "Refactor Hour" where we specifically target blocks of code generated by AI to ensure they follow our internal style guides. It slows us down by 5%, but saves us 50% in future maintenance headaches.
Megan, how do you keep the developers from just "rubber-stamping" the AI code during peer reviews when the sprint pressure is high and everyone wants to close their tickets?
We’ve integrated an AI-driven "Quality Gate" in our CI/CD. It specifically scans for maintainability scores. If the AI-generated code drops below a certain threshold, the build fails automatically.
Automated quality gates are the way to go. It removes the personal bias and ensures the "Scrum velocity" doesn't come at the cost of a crumbling codebase.
Kevin, we actually gamified the process. We have a "Bug Bounty" for catching AI-specific errors during PR reviews. It encourages the team to be more critical. We also use an automated linter that is configured to flag common AI patterns, like over-nesting or missing error boundaries, before it even reaches a human reviewer.