Automated Unit Test Improvement using LLMs at Meta — TestGen-LLM
Existing unit tests in Meta's Android/Kotlin applications left edge cases unaddressed, limiting code coverage and requiring manual effort to improve test suites.
TestGen-LLM enhanced 10% of the classes it was applied to, with most recommendations positively accepted by Meta's software engineers for production, and demonstrated substantial improvements in unit test coverage and code base quality.
Frequently asked questions
What did this team achieve with this AI workflow?
TestGen-LLM enhanced 10% of the classes it was applied to, with most recommendations positively accepted by Meta's software engineers for production, and demonstrated substantial improvements in unit test coverage and…
What tools did this team use?
TestGen-LLM, LLMs.
What results were reported?
Classes enhanced: 10%; Recommendations accepted for production: most recommendations being positively accepted; Test cases reliable with coverage improvements: significant portion found to be reliable and offered tangible improvements in coverage (source-reported, not independently verified).
How is this quality assurance AI workflow structured?
LLM test case generation → Filtration and quality gate → Engineer acceptance review.