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.
翻译:作为开放源码软件项目的维护者,您通常对以拉动请求形式提供的捐款感到非常高兴,从而使项目向前迈出一步。过去的研究显示,在审查拉动请求时,不仅考虑到其内容,而且还考虑到捐助方的社会特征。是否接受捐款以及这需要多长时间,因此不仅取决于捐款的内容。然而,我们目前只有迹象表明,调动机器人请求的优先程度可能较低,即使开发团队明确部署了机器人,并且认为这些机器人是有用的。机器人研发界的一个目标是设计有用的机器人,以便以各种方式有效地支持软件开发。为了接近这一目标,在这项吉特赫布采矿研究中,我们研究了维护者如何与人工生成的人类拉动请求和由机器人自动生成的请求发生互动的可衡量差异。在吉特赫布的所有拉动请求中,大约三分之一目前来自机器人。尽管人类的拉动请求在72.53%中被接受并被合并。所有案例中,设计出有用的机器人研发界的一个目标是设计出有用的机器人,以各种方式有效地支持软件开发。为了更接近这一目标,在GitHub采矿的采矿研究研究研究中,我们检查了这些请求中,这些请求的平均互动程度为更少。