Recommendation of UML Model Conflicts: Unveiling the Biometric Lens for Conflict Resolution - Dalcin, Guilherme; Bolzan, Willian; Lazzari, Luan; Farias, Kleinner (Anais do Simpósio Brasileiro de Engenharia de Software (SBES), 2023)
Model merging assumes a pivotal role in numerous model-centric software development tasks, e.g., evolving UML models to add new features or even reconciling UML models developed collaboratively by distributed development teams. Usually, UML model elements to-be-merged conflict with each other. Unfortunately, resolving conflicts remains a highly cognitive and error-prone task. Today, wearable devices capable of capturing biometric data are a reality. Recent studies indicate that the developer’s cognitive indicators may affect developers while performing development tasks. However, the current literature has neglected the recommendation of conflicts sensitive to the cognitive activities of software developers. This study, therefore, introduces BACR, a biometric-aware approach to recommend UML model conflicts using machine learning. BACR helps UML model merging to push a step forward, recommending model conflicts based on appropriate biometric indicators and using a behavior sequence transformer model. Our approach is based on four scientific institutions. It represents the first effort in supporting the prioritization of cognitively relevant UML model conflicts by developers, mitigating the risk of making incorrect decisions and preventing potential downstream issues.