Using multivariate time series and association rules to detect logical change coupling: An empirical study

In recent years, techniques based on association rules discovery have been extensively used to determine change-coupling relations between artifacts that often changed together. Although association rules worked well in many cases, they fail to capture logical coupling relations between artifacts modified in subsequent change sets. To overcome such a limitation, we propose the use of multivariate time series analysis and forecasting, and in particular the use of Granger causality test, to determine whether a change occurred on a software artifact was consequentially related to changes occurred on some other artifacts. Results of an empirical study performed on four Java and C open source systems show that Granger causality test is able to provide a set of change couplings complementary to association rules, and a hybrid recommender built combining recommendations from association rules and Granger causality is able to achieve a higher recall than the two single techniques.