Mining version histories to guide software changes

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, the mined association rules 1) suggest and predict likely further changes, 2) show up item coupling that...

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Bibliographic Details
Published in:IEEE transactions on software engineering Vol. 31; no. 6; pp. 429 - 445
Main Authors: Zimmermann, T., Zeller, A., Weissgerber, P., Diehl, S.
Format: Journal Article
Language:English
Published: New York IEEE 01.06.2005
IEEE Computer Society
Subjects:
Archives & records
Association rules
classification
clustering
Computer programming
Computer programs
Computer science
Computer Society
configuration management
Data mining
distribution
enhancement
Environmental management
Error analysis
Freeware
History
Index Terms- Programming environments/construction tools
Java
Joining
maintenance
Open source software
Predictions
Programmers
Programming profession
Prototypes
Revisions
Software
Software engineering
Software prototyping
Studies
ISSN:0098-5589, 1939-3520
Online Access:Get full text
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Summary: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, the mined association rules 1) suggest and predict likely further changes, 2) show up item coupling that is undetectable by program analysis, and 3) can prevent errors due to incomplete changes. After an initial change, our ROSE prototype can correctly predict further locations to be changed; the best predictive power is obtained for changes to existing software. In our evaluation based on the history of eight popular open source projects, ROSE's topmost three suggestions contained a correct location with a likelihood of more than 70 percent.
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ISSN:0098-5589
1939-3520
DOI:10.1109/TSE.2005.72