Abstract - Background: Substantial research in the software evolution field aims to recover knowledge about development from the project history that is archived in repositories, such as a Version Control System (VCS). However, the data that is archived in these repositories can be analyzed at different levels of granularity. Although software evolution is a well-studied phenomenon at the revision-level, revisions may be too fine-grained to accurately represent development tasks.
Aim: In this paper, we set out to understand the impact that the revision granularity has on co-change analyses.
Method: We conduct an empirical study of 14 open source systems that are developed by the Apache Software Foundation. To understand the impact that the revision granularity may have on co-change activity, we study work items, i.e., logical groups of revisions that address a single issue.
Results: We find that work item grouping has the potential to impact co-change activity, since 29% of work items consist of 2 or more revisions in 7 of the 14 studied systems. Deeper quantitative analysis shows that, in 7 of the 14 studied systems: (1) 11% of largest work items are entirely composed of small revisions, and would be missed by traditional approaches to filter or analyze large changes, (2) 83% of revisions that co-change under a single work item cannot be grouped using the typical configuration of the sliding time window technique and (3) 48% of work items that involve multiple developers cannot be grouped at the revision-level.
Conclusions: Since the work item granularity is the natural means that practitioners use to separate development tasks, future software evolution studies, especially co-change analyses, should be conducted at the work item level.
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Bibtex
@inproceedings{miura2016esem,
Author = {Keisuke Miura and Shane McIntosh and Yasutaka Kamei and Ahmed E. Hassan and Naoyasu Ubayashi},
Title = {{The Impact of Task Granularity on Co-evolution Analyses}},
Year = {2016},
Booktitle = {Proc. of the International Symposium on Empirical Software Engineering and Measurement (ESEM)},
Pages = {47:1-47:10}
}