An eclectic approach for change impact analysis

Change impact analysis aims at identifying software artifacts being affected by a change. In the past, this problem has been addressed by approaches relying on static, dynamic, and textual analysis. Recently, techniques based on historical analysis and association rules have been explored. This paper proposes a novel change impact analysis method based on the idea that the mutual relationships between software objects can be inferred with a statistical learning approach. We use the bivariate Granger causality test, a multivariate time series forecasting approach used to verify whether past values of a time series are useful for predicting future values of another time series. Results of a preliminary study performed on the Samba daemon show that change impact relationships inferred with the Granger causality test are complementary to those inferred with association rules. This opens the road towards the development of an eclectic impact analysis approach conceived by combining different techniques.