Practitioners’ Expectations on Automated Test Generation
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.
Wed 18 SepDisplayed time zone: Amsterdam, Berlin, Bern, Rome, Stockholm, Vienna change
13:30 - 14:30 | Test GenerationTechnical Papers at EI 9 Hlawka Chair(s): Alessio Gambi Austrian Institute of Technology (AIT) | ||
13:30 20mTalk | Domain Adaptation for Code Model-Based Unit Test Case Generation Technical Papers Jiho Shin York University, Sepehr Hashtroudi University of Calgary, Hadi Hemmati York University, Song Wang York University DOI | ||
13:50 20mTalk | Practitioners’ Expectations on Automated Test Generation Technical Papers Xiao Yu Huawei, Lei Liu Xi’an Jiaotong University, Xing Hu Zhejiang University, Jacky Keung City University of Hong Kong, Xin Xia Huawei, David Lo Singapore Management University DOI | ||
14:10 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 at Davis, Jiabo Huang Tencent, Yuyang Rong University of California at Davis, Yiwen Guo Unaffiliated, Ethan Wang University of California at Davis, Hao Chen University of California at Davis DOI |