  This paper describes Meta's TestGen-LLM tool, which uses LLMs to
automatically improve existing human-written tests. TestGen-LLM verifies that
its generated test classes successfully clear a set of filters that assure
measurable improvement over the original test suite, thereby eliminating
problems due to LLM hallucination. We describe the deployment of TestGen-LLM at
Meta test-a-thons for the Instagram and Facebook platforms. In an evaluation on
Reels and Stories products for Instagram, 75% of TestGen-LLM's test cases built
correctly, 57% passed reliably, and 25% increased coverage. During Meta's
Instagram and Facebook test-a-thons, it improved 11.5% of all classes to which
it was applied, with 73% of its recommendations being accepted for production
deployment by Meta software engineers. We believe this is the first report on
industrial scale deployment of LLM-generated code backed by such assurances of
code improvement.
