Christian Rahe | Searcharxiv

arXiv2026

Smells Depend on the Context: An Interview Study of Issue Tracking Problems and Smells in Practice

Issue Tracking Systems (ITSs) enable software developers and managers to collect and resolve issues collaboratively. While researchers have extensively analysed ITS data to automate or assist specific activities such as issue assignments, duplicate detection, or priority prediction, developer studies on ITSs remain rare. Particularly, little is known about the challenges Software Engineering (SE) teams encounter in ITSs and when certain practices and workarounds (such as leaving issue fields like "priority" empty) are considered problematic. To fill this gap, we conducted an in-depth interview study with 26 experienced SE practitioners from different organisations and industries. We asked them about general problems encountered, as well as the relevance of 31 ITS smells (aka potentially problematic practices) discussed in the literature. By applying Thematic Analysis to the interview notes, we identified 14 common problems including issue findability, zombie issues, workflow bloat, and lack of workflow enforcement. Participants also stated that many of the ITS smells do not occur or are not problematic. Our results suggest that ITS problems and smells are highly dependent on context factors such as ITS configuration, workflow stage, and team size. We also discuss potential tooling solutions to configure, monitor, and visualise ITS smells to cope with these challenges.

cs.SE↗

arXiv2025

How Do Programming Students Use Generative AI?

Programming students have a widespread access to powerful Generative AI tools like ChatGPT. While this can help understand the learning material and assist with exercises, educators are voicing more and more concerns about an overreliance on generated outputs and lack of critical thinking skills. It is thus important to understand how students actually use generative AI and what impact this could have on their learning behavior. To this end, we conducted a study including an exploratory experiment with 37 programming students, giving them monitored access to ChatGPT while solving a code authoring exercise. The task was not directly solvable by ChatGPT and required code comprehension and reasoning. While only 23 of the students actually opted to use the chatbot, the majority of those eventually prompted it to simply generate a full solution. We observed two prevalent usage strategies: to seek knowledge about general concepts and to directly generate solutions. Instead of using the bot to comprehend the code and their own mistakes, students often got trapped in a vicious cycle of submitting wrong generated code and then asking the bot for a fix. Those who self-reported using generative AI regularly were more likely to prompt the bot to generate a solution. Our findings indicate that concerns about potential decrease in programmers' agency and productivity with Generative AI are justified. We discuss how researchers and educators can respond to the potential risk of students uncritically over-relying on Generative AI. We also discuss potential modifications to our study design for large-scale replications.

cs.HC↗