Masum Hasan

Peer code review is a widely adopted software engineering practice to ensure code quality and ensure software reliability in both the commercial and open-source software projects. Due to the large effort overhead associated with practicing code reviews, project managers often wonder, if their code reviews are effective and if there are improvement opportunities in that respect. Since project managers at Samsung Research Bangladesh (SRBD) were also intrigued by these questions, this research developed, deployed, and evaluated a production-ready solution using the Balanced SCorecard (BSC) strategy that SRBD managers can use in their day-to-day management to monitor individual developer's, a particular project's or the entire organization's code review effectiveness. Following the four-step framework of the BSC strategy, we: 1) defined the operation goals of this research, 2) defined a set of metrics to measure the effectiveness of code reviews, 3) developed an automated mechanism to measure those metrics, and 4) developed and evaluated a monitoring application to inform the key stakeholders. Our automated model to identify useful code reviews achieves 7.88% and 14.39% improvement in terms of accuracy and minority class F1 score respectively over the models proposed in prior studies. It also outperforms human evaluators from SRBD, that the model replaces, by a margin of 25.32% and 23.84% respectively in terms of accuracy and minority class F1 score. In our post-deployment survey, SRBD developers and managers indicated that they found our solution as useful and it provided them with important insights to help their decision makings.

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