Mining version histories to guide software changes
Top Cited Papers
- 28 September 2004
- conference paper
- conference paper
- Published by Institute of Electrical and Electronics Engineers (IEEE)
Abstract
We apply data mining to version histories in order to guide programmers along related changes: "Programmers who changed these functions also changed. . . ". Given a set of existing changes, such rules (a) suggest and predict likely further changes, (b) show up item coupling that is indetectable by program analysis, and (c) prevent errors due to incomplete changes. After an initial change, our ROSE prototype can correctly predict 26% of further files to be changed - and 15% of the precise functions or variables. The topmost three suggestions contain a correct location with a likelihood of 64%.Keywords
This publication has 16 references indexed in Scilit:
- CVS release history data for detecting logical couplingsPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2004
- How history justifies system architecture (or not)Published by Institute of Electrical and Electronics Engineers (IEEE) ,2004
- Understanding change-proneness in OO software through visualizationPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2004
- Data mining library reuse patterns in user-selected applicationsPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2003
- Detection of logical coupling based on product release historyPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2002
- Software evolution observations based on product release historyPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2002
- CVSSearch: searching through source code using CVS commentsPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2002
- Simplifying and isolating failure-inducing inputIEEE Transactions on Software Engineering, 2002
- Predicting fault incidence using software change historyIEEE Transactions on Software Engineering, 2000
- Mining generalized association rulesFuture Generation Computer Systems, 1997