Practitioners’ Expectations on Automated Test Generation
This program is tentative and subject to change.
Automated test generation can help developers craft high-quality software tests while mitigating the manual effort needed for writing test code. Despite significant research efforts in automated test generation for nearly 50 years, there is a lack of clarity about what practitioners expect from automated test generation tools and whether the existing research meets their needs. To address this issue, we follow a mixed-methods approach to gain insights into practitioners’ expectations of automated test generation. We first conduct the qualitative analysis from semi-structured interviews with 13 professionals, followed by a quantitative survey of 339 practitioners from 46 countries across five continents. We then conduct a literature review of premier venue papers from 2022 to 2024 (in the last three years) and compare current research findings with practitioners’ expectations. From this comparison, we outline future research directions for researchers to bridge the gap between automated test generation research and practitioners’ expectations.
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 |