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Published Articles >> Table of Contents >> Abstract
26th International Conference on Software Engineering (ICSE'04)
pp. 563-572
Mining Version Histories to Guide Software Changes
Thomas Zimmermann, Saarland University
Peter Weißgerber, Saarland University
Stephan Diehl, Saarland University
Andreas Zeller, Saarland University
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DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/ICSE.2004.1317478
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| Abstract |
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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%.
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 Additional Information
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Citation:
Thomas Zimmermann, Peter Weißgerber, Stephan Diehl, Andreas Zeller,
"Mining Version Histories to Guide Software Changes,"
icse,
pp. 563-572,
26th International Conference on Software Engineering (ICSE'04),
2004
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