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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. The authors 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.
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