Review4Repair: Code review aided automatic program repairing

Learning-based automatic program repair techniques are showing promise to provide quality fix suggestions for detected bugs in the source code of software. These tools mostly exploit historical data buggy and fixed changes heavily dependent on bug localizers while applying a new piece code. With increasing popularity review, dependency can be reduced. Besides, review-based localization is more trustworthy since reviewers’ expertise experience reflected these suggestions. The natural language instructions scripted review comments enormous sources information about bug’s nature expected solutions. However, none learning-based has utilized programming best our knowledge. In this study, we investigate performance improvement using comments. We train sequence-to-sequence model 55,060 reviews associated changes. also introduce tokenization preprocessing approaches that help achieve significant over state-of-the-art techniques. boost top-1 accuracy by 20.33% top-10 34.82%. could suggestion stylistics non-code errors unaddressed prior believe along with generated approach would developers address comment quickly correctly thus save their time effort.