Machine learning for software engineering: case studies in software reuse

  • Di Stefano J
  • Menzies T
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There are many machine learning algorithms currently available. In the 21st century, the problem no longer lies in writing the learner but in choosing which learners to run on a given data set. We argue that the final choice of learners should not be exclusive; in fact, there are distinct advantages in running data sets through multiple learners. To illustrate our point, we perform a case study on a reuse data set using three different styles of learners: association rule, decision tree induction, and treatment. Software reuse is a topic of avid debate in the professional and academic arena; it has proven that it can be both a blessing and a curse. Although there is much debate over where and when reuse should be instituted into a project, our learners found some procedures which should significantly improve the odds of a reuse program succeeding.

Author-supplied keywords

  • association rule
  • case studies
  • data set
  • decision tree induction
  • decision trees
  • learning (artificial intelligence)
  • machine learning
  • software engineering
  • software reusability
  • software reuse
  • treatment learning

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  • J S Di Stefano

  • T Menzies

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