Suggestion Bot: Analyzing the Impact of Automated Suggested Changes on Code Reviews
Abstract
Peer code reviews are crucial for maintaining the quality of the code in software repositories. Developers have introduced a number of software bots to help with the code review process. Despite the benefits of automating code review tasks, many developers face challenges interacting with these bots due to non-comprehensive feedback and disruptive notifications. In this paper, we analyze how incorporating a bot in software development cycle will decrease turnaround time of pull request. We created a bot called SUGGESTION BOT to automatically review the code base using GitHub's suggested changes functionality in order to solve this issue. A preliminary comparative empirical investigation between the utilization of this bot and manual review procedures was also conducted in this study. We evaluate SUGGESTION BOT concerning its impact on review time and also analyze whether the comments given by the bot are clear and useful for users. Our results provide implications for the design of future systems and improving human-bot interactions for code review.
Cite
@article{arxiv.2305.06328,
title = {Suggestion Bot: Analyzing the Impact of Automated Suggested Changes on Code Reviews},
author = {Nivishree Palvannan and Chris Brown},
journal= {arXiv preprint arXiv:2305.06328},
year = {2023}
}