The integration of generative AI tools like ChatGPT into software engineering workflows opens up new opportunities to boost productivity in tasks such as unit test engineering. However, these AI-assisted workflows can also significantly alter the developer’s role, raising concerns about control, output quality, and learning, particularly for novice developers. This study investigates how novice software developers with foundational knowledge in software testing interact with generative AI for engineering unit tests. Our goal is to examine the strategies they use, how heavily they rely on generative AI, and the benefits and challenges they perceive when using generative AI-assisted approaches for test engineering. We conducted an observational study involving 12 undergraduate students who worked with generative AI for unit testing tasks, using ChatGPT running the GPT-3.5 model. We identified four interaction strategies, defined by whether the test idea or the test implementation originated from generative AI or from the participant. Additionally, we singled out prompting styles that focused on one-shot or iterative test generation, which often aligned with the broader interaction strategy. Students reported benefits including time-saving, reduced cognitive load, and support for test ideation, but also noted drawbacks such as diminished trust, test quality concerns, and lack of ownership. While strategy and prompting styles influenced workflow dynamics, they did not significantly affect test effectiveness or test code quality as measured by mutation score or test smells. ... Read more

Modern software systems are large, with a project like Chromium reaching more than 30 million lines of code. Analyzing these large-scale projects over multiple versions rapidly becomes very expensive, and creating tools that can work at this scale is a challenge. This paper presents the HyperAST approach, that exploits the locality and redundancy of source code, to maintain thousands of Syntax Tree (AST) versions in memory. In particular, we contribute a programmatic interface to HyperAST that helps define the incremental computation of code metrics and efficient explorations of the fine-grained abstract syntax representation of source code. ... Read more