On the Evaluation of Large Language Models in Unit Test Generation
This program is tentative and subject to change.
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.
This program is tentative and subject to change.
Thu 31 OctDisplayed time zone: Pacific Time (US & Canada) change
10:30 - 12:00 | |||
10:30 15mTalk | Towards Understanding the Effectiveness of Large Language Models on Directed Test Input Generation Research Papers Zongze Jiang Huazhong University of Science and Technology, Ming Wen Huazhong University of Science and Technology, Jialun Cao Hong Kong University of Science and Technology, Xuanhua Shi Huazhong University of Science and Technology, Hai Jin Huazhong University of Science and Technology | ||
10:45 15mTalk | Distribution-aware Fairness Test Generation Journal-first Papers Sai Sathiesh Rajan Singapore University of Technology and Design, Singapore, Ezekiel Soremekun Royal Holloway, University of London, Yves Le Traon University of Luxembourg, Luxembourg, Sudipta Chattopadhyay Singapore University of Technology and Design | ||
11:00 15mTalk | Effective Unit Test Generation for Java Null Pointer Exceptions Research Papers Myungho Lee Korea University, Jiseong Bak Korea University, Seokhyeon Moon , Yoon-Chan Jhi Technology Research, Samsung SDS, Seoul, South Korea, Hakjoo Oh Korea University | ||
11:15 15mTalk | SlicePromptTest4J: High-coverage Test Generation using LLM via Method Slicing Research Papers Zejun Wang Peking University, Kaibo Liu Peking University, Ge Li Peking University, Zhi Jin Peking University | ||
11:30 15mTalk | DeepREST: Automated Test Case Generation for REST APIs Exploiting Deep Reinforcement Learning Research Papers Davide Corradini University of Verona, Zeno Montolli University of Verona, Michele Pasqua University of Verona, Mariano Ceccato University of Verona | ||
11:45 15mTalk | On the Evaluation of Large Language Models in Unit Test Generation Research Papers Lin Yang Tianjin University, Chen Yang Tianjin University, Shutao Gao Tianjin University, Weijing Wang College of Intelligence and Computing, Tianjin University, Bo Wang Beijing Jiaotong University, Qihao Zhu DeepSeek-AI, Xiao Chu Huawei Cloud Computing Co. Ltd., Jianyi Zhou Huawei Cloud Computing Technologies Co., Ltd., Guangtai Liang Huawei Cloud Computing Technologies, Qianxiang Wang Huawei Technologies Co., Ltd, Junjie Chen Tianjin University | ||