articleIEEE Transactions on Software EngineeringJun 1, 2005Closed access

Mining version histories to guide software changes

TZThomas ZimmermannAZAndreas ZellerPWPeter WeißgerberSDStephan Diehl

Saarland University · Catholic University of America · +1 more institution

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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, 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…

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Topics
Keywords
  • Computer science
  • Set (abstract data type)
  • Software
  • Association rule learning
  • Data mining
  • Open source
  • Open source software
  • Software engineering
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