Bucket's AI Bot Automates Feature Flag Cleanup, Tackling Tech Debt Head-On

The Hidden Cost of Feature Flags and Bucket's AI Fix

Feature flags are essential for controlled rollouts and A/B testing, but they leave a notorious legacy: technical debt. Forgotten flags clutter codebases, create dead paths, and complicate maintenance. Bucket (https://bucket.co) is tackling this pervasive issue head-on with an AI-powered cleanup bot, now entering public beta.

How the AI Cleanup Bot Operates

The bot functions as a GitHub integration:

1. Detection: It scans the codebase for usage of the Bucket SDK, pinpointing specific feature keys.
2. Refactoring: Leveraging Large Language Models (LLMs), it removes the flag logic and associated unreachable code paths.
3. Deployment: Developers receive a pull request. Once reviewed and merged, the flag is automatically archived in Bucket.

"You can get a surprisingly long way with an LLM and a quick prompt, but getting good results, consistently, is where the real work is," notes Bucket's team, encapsulating the core challenge they faced.

The Road to Reliability: Beyond Simple Prompting

Initial results were mixed. While many LLM-generated PRs were functional, inconsistencies plagued early versions:

• Errant comments or whitespace changes
• Unused imports and incorrect indentation
• Failure to remove lone decorators or unused exports
• CI failures due to formatting issues

Bucket's engineers iterated through:

• Advanced Prompt Engineering: Refining instructions for the LLM.
• Model Selection & Switching: Experimenting with different LLMs for specific tasks.
• Custom Solutions: Building targeted logic to handle edge cases LLMs struggled with.

The result, after months of internal and partner testing, is a system generating PRs that are "consistently good" and typically mergeable without manual changes.

Implications for Developers and Tech Debt

This automation represents a tangible step towards reducing a specific, high-friction form of technical debt. For teams heavily utilizing feature flags:

• Reduced Toil: Eliminates manual, error-prone cleanup tasks.
• Code Health: Actively prunes dead code, improving maintainability.
• Focus Shift: Frees developers for higher-value work than flag archaeology.

Try the Beta (React Focused)

Bucket's beta is now open, initially supporting React codebases:

1. Sign up at Bucket
2. Enable the integration: Bucket App Settings

Source & Further Reading:

• Bucket's detailed blog post on building the AI cleanup system: Building AI-Powered Flag Cleanup
• Original announcement discussion: Hacker News Thread

While not a silver bullet for all legacy code, Bucket's AI bot demonstrates a practical application of LLMs to a concrete developer pain point. Its evolution from inconsistent outputs to reliable PRs highlights the crucial shift from AI experimentation to robust engineering required for real-world tools. As feature flag usage grows, automated cleanup may become as vital as the flags themselves.