Machine learning for software engineering: case studies in software reuse

Abstract

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.