Practical guidelines for change recommendation using association rule mining

Leon Moonen, Stefano Di Alesio, David W. Binkley, Thomas Rolfsnes

Abstract

Association rule mining is an unsupervised learning technique that infers relationships among items in a data set. This technique has been successfully used to analyze a system's change history and uncover evolutionary coupling between system artifacts. Evolutionary coupling can, in turn, be used to recommend artifacts that are potentially affected by a given set of changes to the system. In general, the quality of such recommendations is affected by (1) the values selected for various parameters of the mining algorithm, (2) characteristics of the set of changes used to derive a recommendation, and (3) characteristics of the system's change history for which recommendations are generated.

BibTeX

@inproceedings{Moonen-al:ASE16,
  author    = {Leon Moonen and
               Stefano Di Alesio and
               David W. Binkley and
               Thomas Rolfsnes},
  title     = {Practical guidelines for change recommendation using association rule mining},
  booktitle = {ASE},
  pages     = {732--743},
  publisher = {{ACM}},
  year      = {2016},
}

Practical guidelines for change recommendation using association rule mining

Abstract

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