Title: Predicting fault-prone components in a java legacy system
Abstract: This paper reports on the construction and validation of faultproneness prediction models in the context of an object-oriented, evolving, legacy system. The goal is to help QA engineers focus their limited verification resources on parts of the system likely to contain faults. A number of measures including code quality, class structure, changes in class structure, and the history of class-level changes and faults are included as candidate predictors of class fault-proneness. A cross-validated classification analysis shows that the obtained model has less than 20% of false positives and false negatives, respectively. However, as shown in this paper, statistics regarding the classification accuracy tend to inflate the potential usefulness of the fault-proneness prediction models. We thus propose a simple and pragmatic methodology for assessing the costeffectiveness of the predictions to focus verification effort. On the basis of the cost-effectiveness analysis we show that change and fault data from previous releases is paramount to developing a practically useful prediction model. When our model is applied to predict faults in a new release, the estimated potential savings in verification effort is about 29%. In contrast, the estimated savings in verification effort drops to 0% when history data is not included.
Publication Year: 2006
Publication Date: 2006-09-21
Language: en
Type: article
Indexed In: ['crossref']
Access and Citation
Cited By Count: 185
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