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.

中文翻译：

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检测和表征提交代码的机器人

背景：一些传统上手动执行的开发人员活动，例如提交代码、打开、管理或关闭问题，在许多 OSS 项目中越来越多地自动化。具体而言，此类活动通常由对事件做出反应或在特定时间运行的工具执行。我们将此类自动化工具称为机器人，并且在许多与开发人员生产力或代码质量相关的软件挖掘场景中，需要识别机器人以将其行为与个人行为分开。目标：找到一种自动识别机器人和这些机器人提交的代码的方法，并根据其活动模式来表征机器人的类型。方法和结果：我们提出了 BIMAN，这是一种使用作者姓名、提交消息、提交修改的文件来检测机器人的系统方法，和与 ommits 相关的项目。对于我们的测试数据，AUC-ROC 的值为 0.9。我们还根据代码提交的时间模式和修改的文件类型对这些机器人进行了表征，发现它们主要处理文档文件和网页，而这些文件在 HTML 和 JavaScript 生态系统中最为普遍。我们编译了一个可共享的数据集，其中包含有关我们发现的 461 个机器人（所有机器人的提交次数都超过 1000 次）和他们创建的 13,762,430 次提交的详细信息。