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Contribution Details

Type Conference or Workshop Paper
Scope Discipline-based scholarship
Published in Proceedings Yes
Title Improving defect prediction using temporal features and non linear models
Organization Unit
  • Abraham Bernstein
  • Jayalath Ekanayake
  • Martin Pinzger
Presentation Type paper
Item Subtype Original Work
Refereed Yes
Status Published in final form
  • English
Page Range 11 - 18
Event Title Proceedings of the International Workshop on Principles of Software Evolution
Event Type workshop
Event Location Cavtat, Croatia
Event Start Date September 1 - 2007
Event End Date September 1 - 2007
Place of Publication Dubrovnik, Croatia
Publisher IEEE Computer Society
Abstract Text Predicting the defects in the next release of a large software system is a very valuable asset for the pro ject manger to plan her resources. In this paper we argue that temporal features (or aspects) of the data are central to prediction performance. We also argue that the use of non-linear models, as opposed to traditional regression, is necessary to uncover some of the hidden interrelationships between the features and the defects and maintain the accuracy of the prediction in some cases. Using data obtained from the CVS and Bugzilla repositories of the Eclipse pro ject, we extract a number of temporal features, such as the number of revisions and number of reported issues within the last three months. We then use these data to predict both the location of defects (i.e., the classes in which defects will occur) as well as the number of reported bugs in the next month of the pro ject. To that end we use standard tree-based induction algorithms in comparison with the traditional regression. Our non-linear models uncover the hidden relationships between features and defects, and present them in easy to understand form. Results also show that using the temporal features our prediction model can predict whether a source file will have a defect with an accuracy of 99% (area under ROC curve 0.9251) and the number of defects with a mean absolute error of 0.019 (Spearman’s correlation of 0.96).
Other Identification Number merlin-id:2729
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