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Donate arxiv logo > cs > arXiv:2406.18181 [ ] Help | Advanced Search [All fields ] Search arXiv logo Cornell University Logo [ ] GO quick links * Login * Help Pages * About Computer Science > Software Engineering arXiv:2406.18181 (cs) [Submitted on 26 Jun 2024 (v1), last revised 25 Sep 2024 (this version, v2)] Title:On the Evaluation of Large Language Models in Unit Test Generation Authors:Lin Yang, Chen Yang, Shutao Gao, Weijing Wang, Bo Wang, Qihao Zhu, Xiao Chu, Jianyi Zhou, Guangtai Liang, Qianxiang Wang, Junjie Chen View a PDF of the paper titled On the Evaluation of Large Language Models in Unit Test Generation, by Lin Yang and 10 other authors View PDF HTML (experimental) Abstract:Unit testing is an essential activity in software development for verifying the correctness of software components. However, manually writing unit tests is challenging and time-consuming. The emergence of Large Language Models (LLMs) offers a new direction for automating unit test generation. Existing research primarily focuses on closed-source LLMs (e.g., ChatGPT and CodeX) with fixed prompting strategies, leaving the capabilities of advanced open-source LLMs with various prompting settings unexplored. Particularly, open-source LLMs offer advantages in data privacy protection and have demonstrated superior performance in some tasks. Moreover, effective prompting is crucial for maximizing LLMs' capabilities. In this paper, we conduct the first empirical study to fill this gap, based on 17 Java projects, five widely-used open-source LLMs with different structures and parameter sizes, and comprehensive evaluation metrics. Our findings highlight the significant influence of various prompt factors, show the performance of open-source LLMs compared to the commercial GPT-4 and the traditional Evosuite, and identify limitations in LLM-based unit test generation. We then derive a series of implications from our study to guide future research and practical use of LLM-based unit test generation. Comments: Accepted by ASE 2024, Research Paper Track Subjects: Software Engineering (cs.SE) Cite as: arXiv:2406.18181 [cs.SE] (or arXiv:2406.18181v2 [cs.SE] for this version) https://doi.org/10.48550/arXiv.2406.18181 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Lin Yang [view email] [v1] Wed, 26 Jun 2024 08:57:03 UTC (219 KB) [v2] Wed, 25 Sep 2024 06:47:10 UTC (3,326 KB) Full-text links: Access Paper: View a PDF of the paper titled On the Evaluation of Large Language Models in Unit Test Generation, by Lin Yang and 10 other authors * View PDF * HTML (experimental) * TeX Source * Other Formats view license Current browse context: cs.SE < prev | next > new | recent | 2024-06 Change to browse by: cs References & Citations * NASA ADS * Google Scholar * Semantic Scholar a export BibTeX citation Loading... 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