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Disclaimer: The details in this post have been derived from the articles/videos shared online by the Airbnb Engineering Team. All credit for the technical details goes to the Airbnb Engineering Team. The links to the original articles and videos are present in the references section at the end of the post. We’ve attempted to analyze the details and provide our input about them. If you find any inaccuracies or omissions, please leave a comment, and we will do our best to fix them.
Code migrations are usually a slow affair. Dependencies change, frameworks evolve, and teams get stuck rewriting thousands of lines that don’t even change product behavior.
That was the situation at Airbnb.
Thousands of React test files still relied on Enzyme, a tool that hadn’t kept up with modern React patterns. The goal was clear: move everything to React Testing Library (RTL). However, with over 3,500 files in scope, the effort appeared to be a year-long grind of manual rewrites.
Instead, the team finished it in six weeks.
The turning point was the use of AI, specifically Large Language Models (LLMs), not just as assistants, but as core agents in an automated migration pipeline. By breaking the work into structured, per-file steps, injecting rich context into prompts, and systematically tuning feedback loops, the team transformed what looked like a long, manual slog into a fast, scalable process.
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This article unpacks how that migration happened. It covers the structure of the automation pipeline, the trade-offs behind prompt engineering vs. brute-force retries, the methods used to handle complex edge cases, and the results that followed.
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The Need for Migration
Enzyme, adopted in 2015, provided fine-grained access to the internal structure of React components. This approach matched earlier versions of React, where testing internal state and component hierarchy was a common pattern.
By 2020, Airbnb had shifted all new test development to React Testing Library (RTL).
RTL encourages testing components from the perspective of how users interact with them, focusing on rendered output and behavior, not implementation details. This shift reflects modern React testing practices, which prioritize maintainability and resilience to refactoring.
However, thousands of existing test files at Airbnb were still using Enzyme. Migrating them introduced several challenges:
Different testing models: Enzyme relies on accessing component internals. RTL operates at the DOM interaction level. Tests couldn’t be translated line-for-line and required structural rewrites.
Risk of coverage loss: Simply removing legacy Enzyme tests would leave significant gaps in test coverage, particularly for older components no longer under active development.
Manual effort was prohibitive: Early projections estimated over a year of engineering time to complete the migration manually, which was too costly to justify.
The migration was necessary to standardize testing across the codebase and support future React versions, but it had to be automated to be feasible.
Migration Strategy and Proof of Concept
The first indication that LLMs could handle this kind of migration came during a 2023 internal hackathon. A small team tested whether a large language model could convert Enzyme-based test files to RTL. Within days, the prototype successfully migrated hundreds of files. The results were promising in terms of accuracy as well as speed.
That early success laid the groundwork for a full-scale solution. In 2024, the engineering team formalized the approach into a scalable migration pipeline. The goal was clear: automate the transformation of thousands of test files, with minimal manual intervention, while preserving test intent and coverage.
To get there, the team broke the migration process into discrete, per-file steps that could be run independently and in parallel. Each step handled a specific task, like replacing Enzyme syntax, fixing Jest assertions, or resolving lint and TypeScript errors. When a step failed, the system invoked an LLM to rewrite the file using contextual information.
This modular structure made the pipeline easy to debug, retry, and extend. More importantly, it made it possible to run migrations across hundreds of files concurrently, accelerating throughput without sacrificing quality.