Predicting Co-Changed Software Entities in the Context of Software Evolution

Tracing software entity dependencies is a difficult and time-consuming task, and the incomplete changes on software systems are prone to induce bugs. Mining frequent itemset is widely used to find co-changed entities, with which incomplete changes can be detected. In this paper, we present an improved method to predict co-changed software entities in the context of software evolution. In order to extract software entity change transactions precisely, a customized extraction algorithm for change transaction and a fuzzy software entity matching strategy are proposed in our approach, and then Apriori algorithm is reduced to mining the frequent change patterns of software entities efficiently. Experimental results show that our approach can increase the precision by 6%~16%, while the recall reaches 64%.