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ASE 2009

Reducing Features to Improve Bug Prediction

Shivkumar Shivaji, E. James Whitehead Jr., Ram Akella, Sunghun Kim

DOI 10.1109/ase.2009.76dblp

Abstract

Recently, machine learning classifiers have emerged as a way to predict the existence of a bug in a change made to a source code file. The classifier is first trained on software history data, and then used to predict bugs. Two drawbacks of existing classifier-based bug prediction are potentially insufficient accuracy for practical use, and use of a large number of features. These large numbers of features adversely impact scalability and accuracy of the approach. This paper proposes a feature selection technique applicable to classification-based bug prediction. This technique is applied to predict bugs in software changes, and performance of Naive Bayes and Support Vector Machine (SVM) classifiers is characterized.

BibTeX
@inproceedings{Shivaji-al:ASE09,
  author    = {Shivkumar Shivaji and
               E. James Whitehead Jr. and
               Ram Akella and
               Sunghun Kim},
  title     = {Reducing Features to Improve Bug Prediction},
  booktitle = {ASE},
  pages     = {600--604},
  publisher = {{IEEE} Computer Society},
  year      = {2009},
}

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