AI-Driven Code Migrations: Google Reports 89% Time Savings
In internal experiments, Google engineers integrated large language models to accelerate code migrations across dozens of projects. The study found that AI-assisted porting cut the time spent moving code between environments by about 89 percent, turning what used to require months into weeks or days in many cases. Code migration means rewriting software so it runs in a different programming language or framework while preserving behavior and tests. The researchers outline a practical blueprint for modernizing vast software ecosystems, showing tangible gains from AI-assisted workflows. The takeaway is clear: AI can be a practical ally in large-scale modernization, not merely a theoretical option.
The study looked at several complex migrations within a large codebase. It covered the move of identifiers in the Google Ads codebase from 32-bit to 64-bit representations, a project touching more than 500 million lines of code. It also involved upgrading the testing library from JUnit3 to JUnit4 and replacing the Joda Time library with the modern Java time package, java.time. The combination of these changes illustrates how AI can coordinate multiple dimensions of effort, including type upgrades, API evolution, and time handling modernization, all while maintaining testing coverage and expected system behavior, according to Google researchers.
Without AI, porting 32-bit identifiers would demand hundreds of man-years and intense cross‑team coordination. Google’s AI-powered tools automated a large portion of the work. The process began when engineers flagged which identifiers needed updating, and the AI generated the updated code that passed the unit tests. The code then went through the standard review process before integration. This workflow demonstrates a blended approach where AI handles repetitive, pattern-based refactoring while humans verify correctness, ensure alignment with product goals, and validate edge cases, a pattern observed in internal modernization efforts.
About eighty percent of the changes originated from AI, yet manual inspection and adjustments remained part of the workflow. In the JUnit3 to JUnit4 migration, the team changed more than 149 thousand lines of code across 5,359 files in roughly three months, while AI-generated code required no changes in the majority of cases. The move from the Joda Time library to the Java time system produced the largest productivity uplift, with a strong 89 percent increase. These numbers show how AI can accelerate migrations that span modules, tests, and APIs while preserving quality through rigorous testing and human oversight, a pattern many large engineering teams are embracing for modernization tasks. The discussion around AI-powered tooling also hints at broader industry interest in cross-sector partnerships, including talks about collaborations between major platforms and AI developers.
Study authors emphasize that AI complements traditional methods such as abstract syntax tree analysis and pattern searching. While AI carries costs, the results show meaningful reductions in migration time and more code being produced with AI assistance. The approach helps standardize refactoring patterns and supports rapid iteration with test suites, making large-scale modernization more manageable for engineering teams. Earlier discussions suggested a potential merger between a well-known social media platform and a U.S. artificial intelligence company, signaling broader interest in AI-powered tooling and cross-sector partnerships. The trend points to a future where automation assistants become a core part of software development, enabling faster delivery while keeping reliability across diverse technology stacks.