Does this approach only work for test framework migrations?
No — the pattern generalizes to any large-scale, repetitive code transformation: dependency upgrades, language migrations, API version bumps, lint rule adoption across a codebase. The requirements are the same regardless of the transformation: a way to validate each file automatically, and enough structural similarity across files that patterns learned from fixing some of them transfer to fixing the rest.
Answered in
How to Migrate Thousands of Legacy Test Files With LLMs, Not RewritesA step-based pipeline, retry loops instead of perfect prompts, and rich context injection turn a year-long manual migration into a six-week automated one.
Read the full analysisOther questions this article answers
- Why is a large-scale test framework migration usually so slow to do by hand?
- Why did retry loops with error feedback outperform carefully engineered prompts?
- When does a migration pipeline need rich prompt context instead of just retries?
- How do you migrate the last 3% of files that automation can't fully finish?
More development best practices questions
- Why is a large-scale test framework migration usually so slow to do by hand?
- Why did retry loops with error feedback outperform carefully engineered prompts?
- When does a migration pipeline need rich prompt context instead of just retries?
- How do you migrate the last 3% of files that automation can't fully finish?
- What is Project Polaris and how does it relate to GitHub Copilot?
- What architecture does Project Polaris use?
- How does Project Polaris perform compared to GPT-4 Turbo on coding benchmarks?
- Can teams still use GPT-4 Turbo in Copilot after the Polaris rollout?
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