Evidence suggests that Free/Libre Open Source Software (FLOSS) environments provide unlimited learning opportunities. Community members engage in a number of activities both during their interaction with their peers and while making use of the tools available in these environments. A number of studies document the existence of learning processes in FLOSS through the analysis of surveys and questionnaires filled by FLOSS project participants. At the same time, the interest in understanding the dynamics of the FLOSS phenomenon, its popularity and success resulted in the development of tools and techniques for extracting and analyzing data from different FLOSS data sources. This new field is called Mining Software Repositories (MSR). In spite of these efforts, there is limited work aiming to provide empirical evidence of learning processes directly from FLOSS repositories. In this paper, we seek to trigger such an initiative by proposing an approach based on Process Mining to trace learning behaviors from FLOSS participants trails of activities, as recorded in FLOSS repositories, and visualize them as process maps. Process maps provide a pictorial representation of real behavior as it is recorded in FLOSS data. Our aim is to provide critical evidence that boosts the understanding of learning behavior in FLOSS communities by analyzing the relevant repositories. In order to accomplish this, we propose an effective approach that comprises first the mining of FLOSS repositories in order to generate Event logs, and then the generation of process maps, equipped with relevant statistical data interpreting and indicating the value of process discovery from these repos-itories

翻译：证据表明，自由/开源软件（FLOSS）环境提供了无限的学习机会。社区成员在与同伴互动以及利用这些环境中可用工具时，会参与多项活动。已有研究通过分析FLOSS项目参与者填写的调查问卷，记录了FLOSS中学习过程的存在。与此同时，对理解FLOSS现象动态性、流行度及成功因素的兴趣，催生了从不同FLOSS数据源提取和分析数据的工具与技术，这一新领域被称为软件仓库挖掘（MSR）。尽管已有这些努力，但旨在直接从FLOSS仓库中提供学习过程经验证据的研究仍十分有限。本文旨在启动这一方向的研究，提出一种基于过程挖掘的方法，从FLOSS仓库中记录的参与者活动轨迹中追踪学习行为，并将其可视化为过程图。过程图以图形方式呈现FLOSS数据中记录的真实行为。我们的目标是通过分析相关仓库，提供关键证据以促进对FLOSS社区学习行为的理解。为此，我们提出一种有效的方法：首先挖掘FLOSS仓库以生成事件日志，然后生成配备相关统计数据的过程图，这些数据用于解释并指示从这些仓库中进行过程发现的价值。