Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Impact Analysis - Towards a Framework for Comparison
ICSM '93 Proceedings of the Conference on Software Maintenance
Whole program Path-Based dynamic impact analysis
Proceedings of the 25th International Conference on Software Engineering
Mining Version Histories to Guide Software Changes
Proceedings of the 26th International Conference on Software Engineering
MSR '05 Proceedings of the 2005 international workshop on Mining software repositories
Impact Analysis by Mining Software and Change Request Repositories
METRICS '05 Proceedings of the 11th IEEE International Software Metrics Symposium
Generalizing evolutionary coupling with stochastic dependencies
ASE '11 Proceedings of the 2011 26th IEEE/ACM International Conference on Automated Software Engineering
How do software engineers understand code changes?: an exploratory study in industry
Proceedings of the ACM SIGSOFT 20th International Symposium on the Foundations of Software Engineering
Connectivity of co-changed method groups: a case study on open source systems
CASCON '12 Proceedings of the 2012 Conference of the Center for Advanced Studies on Collaborative Research
An incremental points-to analysis with CFL-Reachability
CC'13 Proceedings of the 22nd international conference on Compiler Construction
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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. 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.