Software bots have been facilitating several development activities in Open Source Software (OSS) projects, including code review. However, these bots may bring unexpected impacts to group dynamics, as frequently occurs with new technology adoption. Understanding and anticipating such effects is important for planning and management. To analyze these effects, we investigate how several activity indicators change after the adoption of a code review bot. We employed a regression discontinuity design on 1,194 software projects from GitHub. We also interviewed 12 practitioners, including open-source maintainers and contributors. Our results indicate that the adoption of code review bots increases the number of monthly merged pull requests, decreases monthly non-merged pull requests, and decreases communication among developers. From the developers’ perspective, these effects are explained by the transparency and confidence the bot comments introduce, in addition to the changes in the discussion focused on pull requests. Practitioners and maintainers may leverage our results to understand, or even predict, bot effects on their projects.
What to Expect from Code Review Bots on GitHub?: A Survey with OSS Maintainers
Software bots are used by Open Source Software (OSS) projects to streamline the code review process. Interfacing between developers and automated services, code review bots report continuous integration failures, code quality checks, and code coverage. However, the impact of such bots on maintenance tasks is still neglected. In this paper, we study how project maintainers experience code review bots. We surveyed 127 maintainers and asked about their expectations and perception of changes incurred by code review bots. Our findings reveal that the most frequent expectations include enhancing the feedback bots provide to developers, reducing the maintenance burden for developers, and enforcing code coverage. While maintainers report that bots satisfied their expectations, they also perceived unexpected effects, such as communication noise and newcomers' dropout. Based on these results, we provide a series of implications for bot developers, as well as insights for future research.
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- Award ID(s):
- 1815503
- PAR ID:
- 10287966
- Date Published:
- Journal Name:
- 34th Brazilian Symposium on Software Engineering (SBES 2020)
- Page Range / eLocation ID:
- 457 to 462
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Abstract -
Software bots are used to streamline tasks in Open Source Software (OSS) projects' pull requests, saving development cost, time, and effort. However, their presence can be disruptive to the community. We identified several challenges caused by bots in pull request interactions by interviewing 21 practitioners, including project maintainers, contributors, and bot developers. In particular, our findings indicate noise as a recurrent and central problem. Noise affects both human communication and development workflow by overwhelming and distracting developers. Our main contribution is a theory of how human developers perceive annoying bot behaviors as noise on social coding platforms. This contribution may help practitioners understand the effects of adopting a bot, and researchers and tool designers may leverage our results to better support human-bot interaction on social coding platforms.more » « less
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With the emergence of social coding platforms, collaboration has become a key and dynamic aspect to the success of software projects. In such platforms, developers have to collaborate and deal with issues of collaboration in open-source software development. Although collaboration is challenging, collaborative development produces better software systems than any developer could produce alone. Several approaches have investigated collaboration challenges, for instance, by proposing or evaluating models and tools to support collaborative work. Despite the undeniable importance of the existing efforts in this direction, there are few works on collaboration from perspectives of developers. In this work, we aim to investigate the perceptions of open-source software developers on collaborations, such as motivations, techniques, and tools to support global, productive, and collaborative development. Following an ad hoc literature review, an exploratory interview study with 12 open-source software developers from GitHub, our novel approach for this problem also relies on an extensive survey with 121 developers to confirm or refute the interview results. We found different collaborative contributions, such as managing change requests. Besides, we observed that most collaborators prefer to collaborate with the core team instead of their peers. We also found that most collaboration happens in software development (60%) and maintenance (47%) tasks. Furthermore, despite personal preferences to work independently, developers still consider collaborating with others in specific task categories, for instance, software development. Finally, developers also expressed the importance of the social coding platforms, such as GitHub, to support maintainers, and contributors in making decisions and developing tasks of the projects. Therefore, these findings may help project leaders optimize the collaborations among developers and reduce entry barriers. Moreover, these findings may support the project collaborators in understanding the collaboration process and engaging others in the project.more » « less
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Background: Some developer activity traditionally performed manually, such as making code commits, opening, managing, or closing issues is increasingly subject to automation in many OSS projects. Specifically, such activity is often performed by tools that react to events or run at specific times. We refer to such automation tools as bots and, in many software mining scenarios related to developer productivity or code quality it is desirable to identify bots in order to separate their actions from actions of individuals. Aim: Find an automated way of identifying bots and code committed by these bots, and to characterize the types of bots based on their activity patterns. Method and Result: We propose BIMAN, a systematic approach to detect bots using author names, commit messages, files modified by the commit, and projects associated with the ommits. For our test data, the value for AUC-ROC was 0.9. We also characterized these bots based on the time patterns of their code commits and the types of files modified, and found that they primarily work with documentation files and web pages, and these files are most prevalent in HTML and JavaScript ecosystems. We have compiled a shareable dataset containing detailed information about 461 bots we found (all of whom have more than 1000 commits) and 13,762,430 commits they created.more » « less
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null (Ed.)Recommendations between colleagues are effective for encouraging developers to adopt better practices. Research shows these peer interactions are useful for improving developer behaviors, or the adoption of activities to help software engineers complete programming tasks. However, in-person recommendations between developers in the workplace are declining. One form of online recommendations between developers are pull requests, which allow users to propose code changes and provide feedback on contributions. GitHub, a popular code hosting platform, recently introduced the suggested changes feature, which allows users to recommend improvements for pull requests. To better understand this feature and its impact on recommendations between developers, we report an empirical study of this system, measuring usage, effectiveness, and perception. Our results show that suggested changes support code review activities and significantly impact the timing and communication between developers on pull requests. This work provides insight into the suggested changes feature and implications for improving future systems for automated developer recommendations, such as providing situated, concise, and actionable feedback.more » « less
- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1145/3422392.3422459
