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A comparative analysis of the efficiency of change metrics and static code attributes for defect prediction

Moser, Raimund ; Pedrycz, Witold ; Succi, Giancarlo

2008 ACM/IEEE 30th International Conference on Software Engineering, 2008, Vol.2008 (24), p.181-190

ACM

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  • Título:
    A comparative analysis of the efficiency of change metrics and static code attributes for defect prediction
  • Autor: Moser, Raimund ; Pedrycz, Witold ; Succi, Giancarlo
  • Assuntos: Classification tree analysis ; cost-sensitive classification ; Costs ; defect prediction ; Java ; Logistics ; Permission ; Predictive models ; Resource management ; Software engineering ; Software metrics ; Testing
  • É parte de: 2008 ACM/IEEE 30th International Conference on Software Engineering, 2008, Vol.2008 (24), p.181-190
  • Notas: SourceType-Scholarly Journals-2
    ObjectType-Feature-2
    ObjectType-Conference Paper-1
    content type line 23
    SourceType-Conference Papers & Proceedings-1
    ObjectType-Article-3
  • Descrição: In this paper we present a comparative analysis of the predictive power of two different sets of metrics for defect prediction. We choose one set of product related and one set of process related software metrics and use them for classifying Java files of the Eclipse project as defective respective defect-free. Classification models are built using three common machine learners: logistic regression, Naïve Bayes, and decision trees. To allow different costs for prediction errors we perform cost-sensitive classification, which proves to be very successful: >75% percentage of correctly classified files, a recall of >80%, and a false positive rate <30%. Results indicate that for the Eclipse data, process metrics are more efficient defect predictors than code metrics.
  • Editor: ACM
  • Idioma: Inglês
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  • Realização: ABCD-USP USP   Logos de Redes Sociais: Freepik

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