Automated Unit Test Improvement using Large Language Models at Meta

• URL: http://arxiv.org/abs/2402.09171v1
• Date: Wed, 14 Feb 2024 13:43:14 GMT
• Title: Automated Unit Test Improvement using Large Language Models at Meta
• Authors: Nadia Alshahwan, Jubin Chheda, Anastasia Finegenova, Beliz Gokkaya, Mark Harman, Inna Harper, Alexandru Marginean, Shubho Sengupta, Eddy Wang
• Abstract summary: This paper describes Meta's TestGen-LLM tool, which uses LLMs to automatically improve existing human-written tests. We describe the deployment of TestGen-LLM at Meta test-a-thons for the Instagram and Facebook platforms.
• Score: 44.87533111512982
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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