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

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...

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Published in2008 ACM/IEEE 30th International Conference on Software Engineering Vol. 2008; no. 24; pp. 181 - 190
Main Authors Moser, R., Pedrycz, W., Succi, G.
Format Conference Proceeding Journal Article
LanguageEnglish
Published IEEE 01.01.2008
Subjects
Classification tree analysis
cost-sensitive classification
Costs
defect prediction
Java
Logistics
Permission
Predictive models
Resource management
Software engineering
Software metrics
Testing
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Summary: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, naive 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.
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ISBN:1424444861
9781424444861
1605580791
9781605580791
ISSN:0270-5257
1558-1225
DOI:10.1145/1368088.1368114