Peer code reviews play a critical role in maintaining code quality, and GitHub has introduced several new features to assist with the review process. One of these features is suggested changes, which allows for precise code modifications in pull requests to be suggested in review comments. Despite the availability of such helpful features, many pull requests remain unattended due to lower priority. To address this issue, we developed a bot called ``Suggestion Bot" to automatically review the codebase using GitHub's suggested changes functionality. An empirical study was also conducted to compare the effectiveness of this bot with manual reviews. The findings suggest that implementing this bot can expedite response times and improve the quality of pull request comments for pull-based software development projects. In addition to providing automated suggestions, this feature also offers valuable, concise, and targeted feedback. / Master of Science / Code review, often known as peer review, is a process used to ensure the quality of software. Code review is a process in software development that involves one or more individuals examining the source code of a program, either after it has been implemented or during a pause in the development process. The creator of the code cannot be one of the individuals. "Reviewers" refers to the individuals conducting the checking, excluding the author. However, the majority of reviewers won't have the time to examine and validate the peer's code base, so they'll assign it the lowest priority possible. This could cause pull requests to stall out without being reviewed. Therefore, as part of our research, we are creating a bot called SUGGESTION BOT that provides code changes in pull requests. The author can then accept, reject, or alter these ideas as a necessary component of the pull request. Additionally, we compared the effectiveness of our bot with the manual pull request review procedure, which clearly demonstrated that the incorporation of this bot significantly shortened the turnaround time. Besides giving automated recommendations, this functionality also provides useful, brief, and focused feedback.
| Identifer | oai:union.ndltd.org:VTETD/oai:vtechworks.lib.vt.edu:10919/115635 |
| Date | 03 July 2023 |
| Creators | Palvannan, Nivishree |
| Contributors | Computer Science and Applications, Brown, Dwayne Christian, Gulzar, Muhammad Ali, Meng, Na |
| Publisher | Virginia Tech |
| Source Sets | Virginia Tech Theses and Dissertation |
| Language | English |
| Detected Language | English |
| Type | Thesis |
| Format | ETD, application/pdf |
| Rights | In Copyright, http://rightsstatements.org/vocab/InC/1.0/ |
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