Blogs (9) >>
SPLASH 2016
Sun 30 October - Fri 4 November 2016 Amsterdam, Netherlands
Sun 30 Oct 2016 14:40 - 15:10 at Berlin - Session 3

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

Displayed time zone: Amsterdam, Berlin, Bern, Rome, Stockholm, Vienna change

13:30 - 15:10
Session 3FOSD at Berlin
13:30
30m
Talk
Formula Choice Calculus
FOSD
Spencer Hubbard Oregon State University, USA, Eric Walkingshaw Oregon State University, USA
DOI
14:05
30m
Talk
Implicit Constraints in Partial Feature Models
FOSD
Sofia Ananieva FZI Research Center for Information Technology, Matthias Kowal TU Braunschweig, Germany, Thomas Thüm TU Braunschweig, Germany, Ina Schaefer TU Braunschweig, Germany
DOI
14:40
30m
Talk
Towards Predicting Feature Defects in Software Product Lines
FOSD
Rodrigo Queiroz University of Waterloo, Canada, Thorsten Berger Chalmers University of Technology, Sweden, Krzysztof Czarnecki University of Waterloo, Canada
DOI