Detecting asynchrony and dephase change patterns by mining software repositories

Software maintenance accounts for the largest part of the costs of any program. During maintenance activities, developers implement changes (sometimes simultaneously) on artefacts in order to fix bugs and to implement new requirements. To reduce this part of the costs, previous work proposed approaches to identify the artefacts of programs that change together. These approaches analyse historical data, mined from version control systems, and report change patterns, which lead at the causes, consequences, and actors of the changes to source code files. They also introduce so-called change patterns that describe some typical change dependencies among files. In this paper, we introduce two novel change patterns: the Asynchrony change pattern, corresponding to macro co-changes (MC), i.e., of files that co-change within a large time interval (change periods), and the Dephase change pattern, corresponding to dephase macro co-changes (DC), i.e., macro co-changes that always happen with the same shifts in time. We present our approach, that we named Macocha, to identify these two change patterns in large programs. We use the k-nearest neighbor algorithm to group changes into change periods.We also use the Hamming distance to detect approximate occurrences of Macro co-changes and Dephase macro co-changes. We apply Macocha and compare its performance in terms of precision and recall with UMLDiff (file stability) and Association Rules (co-changing files) on seven systems: ArgoUML, FreeBSD, JFreeChart, Openser, SIP, XalanC, and XercesC, developed with three different languages (C, C++, and Java). These systems have a size ranging from 532 to 1,693 files and during the study period they have undergone 1,555 to 23,944 change commits. We use external information and static analysis to validate (approximate) Macro co-changes and Dephase macro co-changes found byMacocha. Through our case study, we show the existence and usefulness of these novel change patterns to ease software maintenance and, potentially, reduce related costs.