Foundational Imperatives for Process Mining Learning Patterns in Floss Repositories

Patrick Mukala

Abstract


Free/Libre Open Source Software (FLOSS) projects enable groups of participants to work remotely and achieve projects of common purposes. While the phenomenon of FLOSS projects has generated considerable research interest, it still offers extensive potential worth exploring. In particular, in the context of learning, FLOSS communities have been established as environments where successful collaborative and participatory learning between participants occurs. The quality of FLOSS produced applications is indicative of their widespread use and popularity. Given this popularity, it is critical to explore their potential as learning environments. On the other hand, process mining has established itself as a novel approach for empirical analysis of event logs from data repositories. While many studies have provided invaluable insights in this direction, their results are mostly based on surveys and observation reports. In this paper, we lay a foundation to an important discussion that seeks to contribute in this context for the provision of empirical data for learning patterns from FLOSS repositories using process mining.

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