Managing tech debt in large legacy codebases isn’t just a challenge—it’s an ongoing battle that can drain developer productivity and morale. In this talk, I’ll introduce a Python-powered Tech Debt Framework bar-raiser designed to help teams tackle even the most daunting tech debt problems with 100,000+ violations. This open-source framework empowers developers and engineering leaders by: - Tracking Progress: Measure and visualize the state of tech debt and trends over time. - Recognizing Contributions: Celebrate developer efforts and foster accountability with contribution leaderboards and automated shoutouts. - Automating Fixes: Save countless hours with codemods that address repetitive debt patterns, allowing developers to focus on higher-priority work.
Through real-world case studies, I’ll showcase how we: - Reduced 70,000+ pyright-ignore annotations to boost type-checking coverage from 60% to 99.5%. - Converted a monolithic sync codebase to async, addressing blocking IO issues and adopting asyncio effectively.
Attendees will gain actionable strategies for scaling Python automation, fostering team buy-in, and systematically reducing tech debt across massive codebases. Whether you’re dealing with type errors, legacy dependencies, or async transitions, this talk provides a roadmap for creating cleaner, more maintainable code at scale.