Prioritization, Refactoring, and Keeping You Codebase Healthy
summary
Shows concrete prioritization patterns: allocate 5–25% time or a quarter-end buffer week, or run one-week hack weeks; migrate risky libraries feature-by-feature starting with low-traffic simple features (example: replacing RxJava with coroutines); prevent regressions with tooling like a linter that blocks or flags old-pattern PRs and checklist gates; big-company scale examples include a monorepo with ~19,000 objects where converting a few hundred objects yields measurable startup wins and smaller teams (7 engineers, 500k weekly users) pilot safe rollouts.
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chapters (13)
- 0:00Introduction
- 3:21Tech debt planning in teams
- 9:02Convincing stakeholders to refactor
- 12:29Strangler fig pattern
- 16:10Setting refactoring expectations
- 19:39Building coalitions for tech debt
- 22:30Getting help with tech debt
- 26:44Automating tech debt prevention
- 30:40Time vs culture for tech debt
- 37:19Measuring tech debt impact
- 42:17Cultural factors in tech debt
- 45:48Tech debt in non-tech companies
- 46:09Closing and future
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