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Computer Science > Software Engineering

arXiv:2402.09171 (cs)
[Submitted on 14 Feb 2024]

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
Download a PDF of the paper titled Automated Unit Test Improvement
using Large Language Models at Meta, by Nadia Alshahwan and 8 other
authors
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    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.

Comments: 12 pages, 8 figures, 32nd ACM Symposium on the Foundations
          of Software Engineering (FSE 24)
Subjects: Software Engineering (cs.SE)
Cite as:  arXiv:2402.09171 [cs.SE]
          (or arXiv:2402.09171v1 [cs.SE] for this version)
          https://doi.org/10.48550/arXiv.2402.09171
          Focus to learn more
          arXiv-issued DOI via DataCite

Submission history

From: Alexandru Marginean [view email]
[v1] Wed, 14 Feb 2024 13:43:14 UTC (1,490 KB)
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    Improvement using Large Language Models at Meta, by Nadia
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