3 papers
cs.SE2026
On the Viability of Requirements Generation From Code: An Experience Report
Alexander Korn, Jone Bartel, Max Unterbusch +1
Empirical research in Requirements Engineering is hampered by a lack of adequate datasets that pair source code with corresponding requirements. A tempting route to addressing this…
cs.SE2025
On the calibration of Just-in-time Defect Prediction
Xhulja Shahini, Jone Bartel, Klaus Pohl
Just in time defect prediction (JIT DP) leverages ML to identify defect-prone code commits, enabling quality assurance (QA) teams to allocate resources more efficiently by focusing…
cs.LG2023
An AI Chatbot for Explaining Deep Reinforcement Learning Decisions of Service-oriented Systems
Andreas Metzger, Jone Bartel, Jan Laufer
Deep Reinforcement Learning (Deep RL) is increasingly used to cope with the open-world assumption in service-oriented systems. Deep RL was successfully applied to problems such as…