Towards quality assurance in repaired models with PARMOREL

Abstract

Due to the importance of models in the software engineering process, it is crucial to keep them free of errors and assure their quality. As part of our research, we are developing PARMOREL, a tool for personalized and automatic model repair. PARMOREL uses reinforcement learning to find the best sequence of actions for repairing a broken model according to preferences chosen by the user. In this paper, we present a proposal for integrating quality assurance into PARMOREL. We describe an architecture that would allow PARMOREL to learn to automatically repair models with high quality.