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Accelerating Large-Scale Test Migration with LLMs
- Airbnb recently completed a large-scale, LLM-driven code migration of nearly 3.5K React component test files from Enzyme to React Testing Library in just 6 weeks, instead of 1.5 years estimated for manual migration.
- The shift from Enzyme to RTL was necessitated by Enzyme's outdated design and misalignment with modern React testing practices.
- Airbnb's automated migration approach involved using large language models (LLMs) to refactor and validate test files in a step-by-step process.
- Retry loops and dynamic prompting were employed to improve migration success rates, with files reattempted multiple times until validation errors were resolved.
- Rich contextual prompts, including source code, related tests, and project-specific examples, enhanced the LLM's understanding of complex test files.
- Systematic improvement strategies, like stamping files with migration status and targeted re-runs based on failure points, helped address remaining issues in the migration.
- The automation pipeline successfully migrated 75% of target files in 4 hours, with further refinement pushing the completion rate to 97% over four days.
- For the remaining 3% of files, manual intervention based on automation outputs facilitated swift completion of the migration.
- The use of LLMs for large-scale code transformation proved more efficient and cost-effective compared to manual migration estimates.
- Airbnb plans to further leverage LLM-powered automation for code transformations and enhance developer productivity.
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