A multivariate analysis of static code attributes for defect prediction

Burak Turhan, Ayşe Bener

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

32 Citations (Scopus)

Abstract

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
Pages231-237
Number of pages7
DOIs
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

Conference

Conference7th International Conference on Quality Software, QSIC 2007
CountryUnited States of America
CityPortland
Period11/10/0712/10/07

Keywords

  • Defect prediction
  • Naïve bayes
  • Software metrics

Cite this

Turhan, B., & Bener, A. (2007). A multivariate analysis of static code attributes for defect prediction. In Proceedings - 7th International Conference on Quality Software, QSIC 2007 (pp. 231-237). [4385500] https://doi.org/10.1109/QSIC.2007.4385500