Abstract
1 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. Crafting high-quality software tests manually is challenging and time-consuming for developers [46, 71]. To alleviate the issue, researchers have proposed numerous automated test generation techniques or tools for nearly 50 years [55]. However, no prior studies have investigated practitioners' expectations of these techniques or tools. There is a lack of clarity on whether practitioners appreciate the current automated test generation techniques or tools, what factors influence their decisions to adopt them, and what their minimum thresholds for adoption are. Gaining insights from practitioners is essential to uncover critical issues and guide researchers in developing solutions that meet the needs of practitioners. In this paper, we follow a mixed-methods approach to gain insights into practitioners' expectations of automated test generation. We begin with semi-structured interviews with 13 professionals with an average of 6.85 years of software programming experience. Through these interviews, we explore whether the interviewees rely on requirement descriptions or the code under test when crafting testing code in prac