期刊
出版社
ASSOC COMPUTING MACHINERY
DOI: 10.1145/3001867.3001874
关键词
software product lines; features; defect prediction
Defect-prediction techniques can enhance the quality assurance activities for software systems. For instance, they can be used to predict bugs in source files or functions. In the context of a software product line, such techniques could ideally be used for predicting defects in features or combinations of features, which would allow developers to focus quality assurance on the error-prone ones. In this preliminary case study, we investigate how defect prediction models can be used to identify defective features using machine-learning techniques. We adapt process metrics and evaluate and compare three classifiers using an open-source product line. Our results show that the technique can be effective. Our best scenario achieves an accuracy of 73% for accurately predicting features as defective or clean using a Naive Bayes classifier. Based on the results we discuss directions for future work.
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