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Type Conference or Workshop Paper
Scope Discipline-based scholarship
Published in Proceedings Yes
Title Cross-project defect prediction: a large scale experiment on data vs. domain vs. process
Organization Unit
Authors
  • Thomas Zimmermann
  • Nachiappan Nagappan
  • Harald Gall
  • Emanuel Giger
  • Brendan Murphy
Editors
  • V Issarny
  • H van Vliet
Presentation Type paper
Item Subtype Original Work
Refereed Yes
Status Published in final form
Language
  • English
ISBN 978-1-60558-001-2
Page Range 91 - 100
Event Title 7th Joint Meeting of the European Software Engineering Conference and the ACM SIGSOFT Symposium on the Foundations of Software Engineering
Event Type conference
Event Location Amsterdam, The Netherlands
Event Start Date August 24 - 2009
Event End Date August 28 - 2009
Place of Publication New York, NY, USA
Publisher ACM
Abstract Text Prediction of software defects works well within projects as long as there is a sufficient amount of data available to train any models. However, this is rarely the case for new software projects and for many companies. So far, only a few have studies focused on transferring prediction models from one project to another. In this paper, we study cross-project defect prediction models on a large scale. For 12 real-world applications, we ran 622 cross-project predictions. Our results indicate that cross-project prediction is a serious challenge, i.e., simply using models from projects in the same domain or with the same process does not lead to accurate predictions. To help software engineers choose models wisely, we identified factors that do influence the success of cross-project predictions. We also derived decision trees that can provide early estimates for precision, recall, and accuracy before a prediction is attempted.
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Digital Object Identifier 10.1145/1595696.1595713
Other Identification Number merlin-id:165
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