ReFixar: Multi-version Reasoning for Automated Repair of Regression Errors

Software programs evolve naturally as part of the ever-changing customer needs and fast-paced market. Software evolution, however, often introduces regression bugs, which un-duly break previously working functionalities of the software. To repair regression bugs, one needs to know when and where a bug emerged from, e.g., the bug-inducing code changes, to narrow down the search space. Unfortunately, existing state-of-the-art automated program repair (APR) techniques have not yet fully exploited this information, rendering them less efficient and effective to navigate through a potentially large search space containing many plausible but incorrect solutions. In this work, we revisit APR on repairing regression errors in Java programs. We empirically show that existing state-of-the-art APR techniques do not perform well on regression bugs due to their algorithm design and lack of knowledge on bug inducing changes. We subsequently present ReFixar, a novel repair technique that leverages software evolution history to generate high quality patches for Java regression bugs. The key novelty that empowers ReFixar to more efficiently and effectively traverse the search space is two-fold: (1) A systematic way for multi-version reasoning to capture how a software evolves through its history, and (2) A novel search algorithm over a set of generic repair templates, derived from the principle of incorrectness logic and informed by both past bug fixes and their bug-inducing code changes; this enables ReFixar to achieve a balance of both genericity and specificity, i.e., generic common fix patterns of bugs and their specific contexts. We compare ReFixar against the state-of-the-art APR techniques on a data set of 51 real regression bugs from 28 large real-world programs. Experiments show that ReFixar significantly outperforms the best baseline by a large margin, i.e., ReFixar can fix correctly 24 bugs while the best baseline can only correctly fix 9 bugs.