Domain Adaptation for Code Model-based Unit Test Case Generation
This program is tentative and subject to change.
Recently, deep learning-based test case generation approaches have been proposed to automate the generation of unit test cases. In this study, we leverage Transformer-based code models to generate unit tests with the help of Domain Adaptation (DA) at a project level. Specifically, we use CodeT5, a relatively small language model trained on source code data, and fine-tune it on the test generation task. Then, we apply domain adaptation to each target project data to learn project-specific knowledge (project-level DA). We use the Methods2test dataset to fine-tune CodeT5 for the test generation task and the Defects4j dataset for project-level domain adaptation 18 and evaluation. We compare our approach with (a) CodeT5 fine-tuned on the test generation without DA, (b) the A3Test tool, and (c) GPT-4 on five projects from the Defects4j dataset. The results show that tests generated using DA can increase the line coverage by 18.62%, 19.88%, and 18.02% and mutation score by 16.45%, 16.01%, and 12.99% compared to the above (a), (b), and (c) baselines, respectively. The overall results show consistent improvements in metrics such as parse rate, compile rate, BLEU, and CodeBLEU. In addition, we show that our approach can be seen as a complementary solution alongside existing search-based test generation tools, such as EvoSuite, to increase the overall coverage and mutation scores with an average of 34.42% and 6.8% for line coverage and mutation score, respectively.
This program is tentative and subject to change.
Wed 18 SepDisplayed time zone: Amsterdam, Berlin, Bern, Rome, Stockholm, Vienna change
13:30 - 14:50 | |||
13:30 20mTalk | LLM4Fin: Fully Automating LLM-Powered Test Case Generation for FinTech Software Acceptance Testing Technical Papers Zhiyi Xue East China Normal University, Liangguo East China Normal University, Senyue Tian East China Normal University, Xiaohong Chen ECNU, Pingping Li Guotai Junan Securities Co., Ltd, Liangyu Chen East China Normal University, Tingting Jiang Guotai Junan Securities Co., Ltd, Min Zhang East China Normal University | ||
13:50 20mTalk | Domain Adaptation for Code Model-based Unit Test Case Generation Technical Papers Jiho Shin York University, Sepehr Hashtroudi , Hadi Hemmati York University, Song Wang York University | ||
14:10 20mTalk | Practitioners’ Expectations on Automated Test Generation Technical Papers | ||
14:30 20mTalk | UniTSyn: A Large-Scale Dataset Capable of Enhancing the Prowess of Large Language Models for Program Testing Technical Papers Yifeng He University of California, Davis, Jiabo Huang Tencent, Yuyang Rong University of California, Davis, Yiwen Guo Unaffiliated, Ethan Wang University of California, Davis, Hao Chen University of California, Davis |