Data mining for software process discovery in open source software development communities

Abstract

Software process discovery has historically been an intensive task, either done through exhaustive empirical studies or in an automated fashion using techniques such as logging and analysis of command shell operations. While empirical studies have been fruitful, data collection has proven to be tedious and time consuming. Existing automated approaches have expedited collection of fine-grained data, but do so at the cost of impinging on the developer's work environment, few of who may be observed. In this paper, we explore techniques for discovering development processes from publicly available open source software development repositories that exploit advances in artificial intelligence. Our goal is to facilitate process discovery in ways that are less cumbersome than empirical techniques and offer a more holistic, task-oriented view of the process than current automated systems provide.