Software defect prediction: Heuristics for weighted naïve bayes

Burak Turhan, Ayşe Bener

Research output: Contribution to conferencePaperpeer-review

33 Citations (Scopus)


Defect prediction is an important topic in software quality research. Statistical models for defect prediction can be built on project repositories. Project repositories store software metrics and defect information. This information is then matched with software modules. Naive Bayes is a well known, simple statistical technique that assumes the 'independence' and 'equal importance' of features, which are not true in many problems. However, Naive Bayes achieves high performances on a wide spectrum of prediction problems. This paper addresses the 'equal importance' of features assumption of Naive Bayes. We propose that by means of heuristics we can assign weights to features according to their importance and improve defect prediction performance. We compare the weighted Naive Bayes and the standard Naive Bayes predictors' performances on publicly available datasets. Our experimental results indicate that assigning weights to software metrics increases the prediction performance significantly.

Original languageEnglish
Number of pages6
Publication statusPublished - 1 Dec 2007
Externally publishedYes
EventInternational Conference on Software and Data Technologies 2007 - Barcelona, Spain
Duration: 22 Jul 200725 Jul 2007
Conference number: 2nd (Proceedings)


ConferenceInternational Conference on Software and Data Technologies 2007
Abbreviated titleICSOFT 2007
Internet address


  • Defect prediction
  • Empirical software engineering
  • Feature weighting
  • Naïve bayes
  • Software metrics
  • Software quality

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