Feature Extraction, Learning and Selection in Support of Patch Correctness Assessment

Viktor Csuvik, Dániel Horváth, László Vidács

Abstract: Automated Program Repair (APR) strives to minimize the expense associated with manual bug fixing by developing methods where patches are generated automatically and then validated against an oracle, such as a test suite. However, due to the potential imperfections in the oracle, patches validated by it may still be incorrect. A significant portion of the literature on APR focuses on this issue, usually referred to as Patch Correctness Check (PCC). Several approaches have been proposed that use a variety of information from the project under repair, such as diverse manually designed heuristics or learned embedding vectors. In this study, we explore various features obtained from previous studies and assess their effectiveness in identifying incorrect patches. We also evaluate the potential for accurately classifying correct patches by combining and selecting learned embeddings with engineered features, using various Machine Learning (ML) models. Our experiments demonstrate that not al