Analysis of Naive Bayes' assumptions on software fault data: An empirical study

Burak Turhan, Ayse Bener

Research output: Contribution to journalArticleResearchpeer-review

115 Citations (Scopus)


Software defect prediction is important for reducing test times by allocating testing resources effectively. In terms of predicting the defects in software, Naive Bayes outperforms a wide range of other methods. However, Naive Bayes assumes the 'independence' and 'equal importance' of attributes. In this work, we analyze these assumptions of Naive Bayes using public software defect data from NASA. Our analysis shows that independence assumption is not harmful for software defect data with PCA pre-processing. Our results also indicate that assigning weights to static code attributes may increase the prediction performance significantly, while removing the need for feature subset selection.

Original languageEnglish
Pages (from-to)278-290
Number of pages13
JournalData and Knowledge Engineering
Issue number2
Publication statusPublished - 1 Feb 2009
Externally publishedYes


  • Empirical study
  • Naive Bayes
  • Software defect prediction

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