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Mining version histories to guide software changes

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4 Author(s)
Zimmermann, T. ; Saarland Univ., Saarbrucken, Germany ; Weibgerber, P. ; Diehl, S. ; Zeller, A.

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%.

Published in:

Software Engineering, 2004. ICSE 2004. Proceedings. 26th International Conference on

Date of Conference:

23-28 May 2004

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