Mining Version Histories to Guide Software Changes

  • Authors:
  • Thomas Zimmermann;Peter Weisgerber;Stephan Diehl;Andreas Zeller

  • Affiliations:
  • Saarland University;Saarland University;Saarland University;Saarland University

  • Venue:
  • Proceedings of the 26th International Conference on Software Engineering
  • Year:
  • 2004

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Abstract

We apply data mining to version histories in order toguide programmers along related changes: "Programmerswho changed these functions also changed. . . ". Given aset of existing changes, such rules (a) suggest and predictlikely further changes, (b) show up item coupling that is indetectableby program analysis, and (c) prevent errors dueto incomplete changes. After an initial change, our ROSEprototype can correctly predict 26% of further files to bechanged 驴 and 15% of the precise functions or variables.The topmost three suggestions contain a correct locationwith a likelihood of 64%.