Constrained Feature Selection for Localizing Faults
Conference Proceeding Article
Developers often take much time and effort to find buggy program elements. To help developers debug, many past studies have proposed spectrum-based fault localization techniques. These techniques compare and contrast correct and faulty execution traces and highlight suspicious program elements. In this work, we propose constrained feature selection algorithms that we use to localize faults. Feature selection algorithms are commonly used to identify important features that are helpful for a classification task. By mapping an execution trace to a classification instance and a program element to a feature, we can transform fault localization to the feature selection problem. Unfortunately, existing feature selection algorithms do not perform too well, and we extend its performance by adding a constraint to the feature selection formulation based on a specific characteristic of the fault localization problem. We have performed experiments on a popular benchmark containing 154 faulty versions from 8 programs and demonstrate that several variants of our approach can outperform many fault localization techniques proposed in the literature. Using Wilcoxon rank-sum test and Cliff's d effect size, we also show that the improvements are both statistically significant and substantial.
Software and Cyber-Physical Systems
2015 IEEE 31st International Conference on Software Maintenance and Evolution (ICSME): September 29-October 1, 2015, Bremen, Gemany: Proceedings
City or Country
LE, Tien-Duy B.; LO, David; and LI, Ming.
Constrained Feature Selection for Localizing Faults. (2015). 2015 IEEE 31st International Conference on Software Maintenance and Evolution (ICSME): September 29-October 1, 2015, Bremen, Gemany: Proceedings. 501-505. Research Collection School Of Information Systems.
Available at: http://ink.library.smu.edu.sg/sis_research/3088