In a bold demonstration of AI’s growing role in software development, Airbnb has successfully completed a large-scale code migration project using large language models (LLMs), dramatically reducing the timeline from an estimated 1.5 years to just six weeks.
The project involved updating approximately 3,500 React component test files from Enzyme to the more modern React Testing Library (RTL).
According to Airbnb software engineer Charles Covey-Brandt, the company’s AI-driven pipeline used a combination of automated validation steps and frontier LLMs to handle the bulk of the transformation. Impressively, 75% of the files were migrated within just four hours, thanks to robust automation and intelligent retries powered by dynamic prompt engineering with context-rich inputs of up to 100,000 tokens.
Despite this efficiency, about 900 files initially failed validation. Airbnb employed iterative tools and a status-tracking system to bring that number down to fewer than 100, which were finally resolved manually—underscoring the continued need for human intervention in such processes.
Other tech giants echo this hybrid approach. Google, in a recent report, noted a 50% speed increase in migrating codebases using LLMs.
One project converting ID types in the Google Ads system—originally estimated to take hundreds of engineering years—was largely automated, with 80% of code changes authored by AI. However, inaccuracies still required manual edits, prompting Google to invest further in AI-powered verification.
Amazon Web Services also highlighted the importance of human-AI collaboration in code migration.
Its research into modernizing Java code using Amazon Q revealed that developers value control and remain cautious of AI outputs. Participants emphasized their role as reviewers, citing concerns about incorrect or misleading changes.
While AI is accelerating what were once laborious coding tasks, these case studies reveal that full autonomy remains out of reach.
Engineers continue to act as crucial gatekeepers, validating and refining AI-generated code. For now, the future of code migration lies in intelligent partnerships—where LLMs do the heavy lifting and humans ensure precision.
