2023/07/15 by Patrick Mukala, Mukala, Patrick, António Cerone +3
Business, Management and Accounting · Computer Science · #Business Process Modeling and Analysis #Computers and Society (cs.CY) #F.2.2 #FOS: Computer and information sciences #I.2.7 #Open Source Software Innovations #Software Engineering (cs.SE) #Software Engineering Research
paper · pdf · doi:10.48550/arxiv.2307.07841
openalex publication_date 2023/07/15 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Evidence suggests that Free/Libre Open Source Software (FLOSS) environments\nprovide unlimited learning opportunities. Community members engage in a number\nof activities both during their interaction with their peers and while making\nuse of the tools available in these environments. A number of studies document\nthe existence of learning processes in FLOSS through the analysis of surveys\nand questionnaires filled by FLOSS project participants. At the same time, the\ninterest in understanding the dynamics of the FLOSS phenomenon, its popularity\nand success resulted in the development of tools and techniques for extracting\nand analyzing data from different FLOSS data sources. This new field is called\nMining Software Repositories (MSR). In spite of these efforts, there is limited\nwork aiming to provide empirical evidence of learning processes directly from\nFLOSS repositories. In this paper, we seek to trigger such an initiative by\nproposing an approach based on Process Mining to trace learning behaviors from\nFLOSS participants trails of activities, as recorded in FLOSS repositories, and\nvisualize them as process maps. Process maps provide a pictorial representation\nof real behavior as it is recorded in FLOSS data. Our aim is to provide\ncritical evidence that boosts the understanding of learning behavior in FLOSS\ncommunities by analyzing the relevant repositories. In order to accomplish\nthis, we propose an effective approach that comprises first the mining of FLOSS\nrepositories in order to generate Event logs, and then the generation of\nprocess maps, equipped with relevant statistical data interpreting and\nindicating the value of process discovery from these repos-itories\n