Towards Predicting Feature Defects in Software Product Lines
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.
Sun 30 Oct
|13:30 - 14:00|
|14:05 - 14:35|
Sofia AnanievaFZI Research Center for Information Technology, Matthias KowalTU Braunschweig, Germany, Thomas ThümTU Braunschweig, Germany, Ina SchaeferTU Braunschweig, GermanyDOI
|14:40 - 15:10|
Rodrigo QueirozUniversity of Waterloo, Canada, Thorsten BergerChalmers University of Technology, Sweden, Krzysztof CzarneckiUniversity of Waterloo, CanadaDOI