A multivariate analysis of static code attributes for defect prediction

Burak Turhan, Ayşe Bener

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review

39 Citations (Scopus)


Defect prediction is important in order to reduce test times by allocating valuable test resources effectively. In this work, we propose a model using multivariate approaches in conjunction with Bayesian methods for defect predictions. The motivation behind using a multivariate approach is to overcome the independence assumption of univariate approaches about software attributes. Using Bayesian methods gives practitioners an idea about the defectiveness of software modules in a probabilistic framework rather than the hard classification methods such as decision trees. Furthermore the software attributes used in this work are chosen among the static code attributes that can easily be extracted from source code, which prevents human errors or subjectivity. These attributes are preprocessed with feature selection techniques to select the most relevant attributes for prediction. Finally we compared our proposed model with the best results reported so far on public dataseis and we conclude that using multivariate approaches can perform better.

Original languageEnglish
Title of host publicationProceedings - 7th International Conference on Quality Software, QSIC 2007
PublisherIEEE, Institute of Electrical and Electronics Engineers
Number of pages7
ISBN (Print)0769530354, 9780769530352
Publication statusPublished - 1 Dec 2007
Externally publishedYes
Event7th International Conference on Quality Software, QSIC 2007 - Portland, United States of America
Duration: 11 Oct 200712 Oct 2007


Conference7th International Conference on Quality Software, QSIC 2007
Country/TerritoryUnited States of America


  • Defect prediction
  • Naïve bayes
  • Software metrics

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