On the Evaluation of Large Language Models in Unit Test Generation

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. Existi...

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Vydáno v:IEEE/ACM International Conference on Automated Software Engineering : [proceedings] s. 1607 - 1619
Hlavní autoři: Yang, Lin, Yang, Chen, Gao, Shutao, Wang, Weijing, Wang, Bo, Zhu, Qihao, Chu, Xiao, Zhou, Jianyi, Liang, Guangtai, Wang, Qianxiang, Chen, Junjie
Médium: Konferenční příspěvek
Jazyk:angličtina
Vydáno: ACM 27.10.2024
Témata:
Codes
Defect detection
Empirical Study
Large Language Model
Large language models
Software
Software engineering
Software testing
Syntactics
Test pattern generators
Training data
Unit Test Generation
Writing
ISSN:2643-1572
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Shrnutí: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.CCS CONCEPTS * Software and its engineering → Software testing and debugging.
ISSN:2643-1572
DOI:10.1145/3691620.3695529