Reinforcement Learning from Automatic Feedback for High-Quality Unit Test Generation
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| Publicat a: | arXiv.org (Dec 18, 2024), p. n/a |
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| Altres autors: | , , |
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Cornell University Library, arXiv.org
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| Accés en línia: | Citation/Abstract Full text outside of ProQuest |
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|---|---|---|---|
| 001 | 3147568761 | ||
| 003 | UK-CbPIL | ||
| 022 | |a 2331-8422 | ||
| 035 | |a 3147568761 | ||
| 045 | 0 | |b d20241218 | |
| 100 | 1 | |a Steenhoek, Benjamin | |
| 245 | 1 | |a Reinforcement Learning from Automatic Feedback for High-Quality Unit Test Generation | |
| 260 | |b Cornell University Library, arXiv.org |c Dec 18, 2024 | ||
| 513 | |a Working Paper | ||
| 520 | 3 | |a Software testing is a crucial but time-consuming aspect of software development, and recently, Large Language Models (LLMs) have gained popularity for automated test case generation. However, because LLMs are trained on vast amounts of open-source code, they often generate test cases that do not adhere to best practices and may even contain test smells (anti-patterns). To address this issue, we propose Reinforcement Learning from Static Quality Metrics (RLSQM), wherein we utilize Reinforcement Learning to generate high-quality unit tests based on static analysis-based quality metrics. First, we analyzed LLM-generated tests and show that LLMs frequently do generate undesirable test smells -- up to 37% of the time. Then, we implemented lightweight static analysis-based reward model and trained LLMs using this reward model to optimize for five code quality metrics. Our experimental results demonstrate that the RL-optimized Codex model consistently generated higher-quality test cases than the base LLM, improving quality metrics by up to 23%, and generated nearly 100% syntactically-correct code. RLSQM also outperformed GPT-4 on all code quality metrics, in spite of training a substantially cheaper Codex model. We provide insights into how reliably utilize RL to improve test generation quality and show that RLSQM is a significant step towards enhancing the overall efficiency and reliability of automated software testing. Our data are available at https://doi.org/10.6084/m9.figshare.25983166. | |
| 653 | |a Software reliability | ||
| 653 | |a Best practice | ||
| 653 | |a Source code | ||
| 653 | |a Static code analysis | ||
| 653 | |a Large language models | ||
| 653 | |a Automation | ||
| 653 | |a Software development | ||
| 653 | |a Software testing | ||
| 700 | 1 | |a Tufano, Michele | |
| 700 | 1 | |a Sundaresan, Neel | |
| 700 | 1 | |a Svyatkovskiy, Alexey | |
| 773 | 0 | |t arXiv.org |g (Dec 18, 2024), p. n/a | |
| 786 | 0 | |d ProQuest |t Engineering Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3147568761/abstract/embedded/ZKJTFFSVAI7CB62C?source=fedsrch |
| 856 | 4 | 0 | |3 Full text outside of ProQuest |u http://arxiv.org/abs/2412.14308 |