Release Pattern Discovery via Partitioning: Methodology and Case Study

Abstract

The development of Open Source systems produces a variety of software artifacts such as source code, version control records, bug reports, and email discussions. Since the development is distributed across different tool environments and developer practices, any analysis of project behavior must be inferred from whatever common artifacts happen to be available. In this paper, we propose an approach to characterizing a project's behavior around the time of major and minor releases; we do this by partitioning the observed activities, such as artifact check-ins, around the dates of major and minor releases, and then look for recognizable patterns. We validate this approach by means of a case study on the MySQL database system; in this case study, we found patterns which suggested MySQL was behaving consistently within itself. These patterns included testing and documenting that took place more before a release than after and that the rate of source code changes dipped around release time.