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.

Abstract.Refactoring is a hot and controversial issue. Supporters claim that it helps increasing the quality of the code, making it easier to understand, modify and maintain. Moreover, there are also claims that refactoring yields higher development productivity -however, there is only limited empirical evidence of such assumption. A case study has been conducted to assess the impact of refactoring in a close-to industrial environment. Results indicate that refactoring not only increases aspects of software quality, but also improves productivity. Our findings are applicable to small teams working in similar, highly volatile domains (ours is application development for mobile devices). However, additional research is needed to ensure that this is indeed true and to generalize it to other contexts.

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