Data-mining in Support of Detecting Class Co-evolution

In an evolving system maintained over a long time period, there exist many non-trivial relationships among system classes, such as class co-evolutions, which usually are not easily perceivable in the source code. However, unfortunately, the continuing evolution of large, long-lived systems leads to lost information about these hidden relationships. In this paper, we propose a method for recovering such lost knowledge by data mining method. This method relies on the UMLDiff algorithm that, given a sequence of UML class models of a system, surfaces the design-level changes over its life span, thus eliminating the need for high quality modification reports and nonintuitive software code-based metrics. We employ Apriori association rule mining algorithm to the transactional database of class modifications, which elicit previously unknown or undocumented co-evolving relations among two or more classes. The recovered knowledge facilitates the overall understanding of system evolution and the planning of future maintaining activities. We report on one real world case study evaluating our approach.