Bots Don't Mind Waiting, Do They? Comparing the Interaction With Automatically and Manually Created Pull Requests (2103.03591v1)

Abstract: As a maintainer of an open source software project, you are usually happy about contributions in the form of pull requests that bring the project a step forward. Past studies have shown that when reviewing a pull request, not only its content is taken into account, but also, for example, the social characteristics of the contributor. Whether a contribution is accepted and how long this takes therefore depends not only on the content of the contribution. What we only have indications for so far, however, is that pull requests from bots may be prioritized lower, even if the bots are explicitly deployed by the development team and are considered useful. One goal of the bot research and development community is to design helpful bots to effectively support software development in a variety of ways. To get closer to this goal, in this GitHub mining study, we examine the measurable differences in how maintainers interact with manually created pull requests from humans compared to those created automatically by bots. About one third of all pull requests on GitHub currently come from bots. While pull requests from humans are accepted and merged in 72.53% of all cases, this applies to only 37.38% of bot pull requests. Furthermore, it takes significantly longer for a bot pull request to be interacted with and for it to be merged, even though they contain fewer changes on average than human pull requests. These results suggest that bots have yet to realize their full potential.

• The paper finds that bot-generated pull requests have significantly lower merge rates (37.38% vs 72.53%) and much longer time to first interaction (12.3 hours vs 14 minutes) compared to human-created ones.
• Key findings suggest a potential bias, trust deficit, or suboptimal integration strategies affecting how maintainers interact with automated contributions.
• The research highlights the need for optimized interaction models, refined bot integration strategies, or design enhancements to improve the acceptance and efficiency of bot contributions.

Comparative Analysis of Interactions with Bot and Human Generated Pull Requests

The research paper titled "Bots Don’t Mind Waiting, Do They? Comparing the Interaction With Automatically and Manually Created Pull Requests" provides a thorough analysis of the disparate treatment of pull requests (PRs) by bots and humans on GitHub platforms. This investigative paper employs GitHub mining techniques to quantitatively compare how maintainers interact with PRs generated manually by developers versus those automatically created by bots. The findings have potential implications for the effectiveness and integration of software bots in open-source environments.

Key Findings

The paper reveals that approximately one-third of all pull requests originate from bots, yet these PRs exhibit a 37.38% merge rate, significantly lower than the 72.53% acceptance rate for human-created PRs. The time to first interaction and merge is substantially delayed for bot-generated PRs, showcasing an average of 12.3 hours compared to 14 minutes for human PRs. Despite being smaller in scale, bot-generated PRs receive minimal interaction, with meager comment engagement compared to their human counterparts. The paper points to a clear disparity in the responsiveness and prioritization of bot contributions, a critical observation given that bots are designed to expedite and streamline various development processes.

Implications and Speculations

These findings are indicative of potential underutilization of software bots' capabilities, which presents both theoretical and practical challenges. On the user front, the delayed acceptance and reduced engagement with bot-generated PRs suggest a potential bias or a trust deficit toward automated contributions, potentially stifling the adoption and productivity gains bots aim to deliver. Practically, this poses a compelling proposition: either the integration strategy for bots needs refinement to ensure prompt handling of bot contributions, or bots themselves need design enhancements to match human levels of engagement.

From a theoretical perspective, the paper’s outcomes challenge current perceptions of automation in software development, emphasizing the need for further exploration into the socio-technical interaction dynamics within the development community. Future research can build upon these insights to articulate a framework for designing bots that seamlessly integrate into human workflows, minimizing friction and maximizing their support capabilities.

Future Directions

Further research could be grounded on this paper’s foundation by delving deeper into why discrepancies in engagement occur beyond the quantitative metrics, possibly inspecting qualitative dimensions such as content differences or the impact of the perceived importance of tasks handled by bots. Additionally, evaluating individual bots’ performance and their specific interaction characteristics could yield valuable guidelines for optimizing bot contributions' acceptance and efficiency.

In conclusion, this paper offers substantial data-driven insight into how current practices may disadvantage bot contributions and highlights the pressing need for an optimized interaction model that appreciates the evolving role of bots in facilitating software development. The implications extend beyond mere statistics to encompass organizational change in development operations, presenting both a challenge and opportunity for future innovations in automation and integration within software ecosystems.