Leveraging Qualitative Reasoning to Improve SFL

Leveraging Qualitative Reasoning to Improve SFL

Alexandre Perez, Rui Abreu

Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence
Main track. Pages 1935-1941. https://doi.org/10.24963/ijcai.2018/267

Spectrum-based fault localization (SFL) correlates a system's components with observed failures. By reasoning about coverage, SFL allows for a lightweight way of pinpointing faults. This abstraction comes at the cost of missing certain faults, such as errors of omission, and failing to provide enough contextual information to explain why components are considered suspicious. We propose an approach, named Q-SFL, that leverages qualitative reasoning to augment the information made available to SFL techniques. It qualitatively partitions system components, and treats each qualitative state as a new SFL component to be used when diagnosing. Our empirical evaluation shows that augmenting SFL with qualitative components can improve diagnostic accuracy in 54% of the considered real-world subjects.
Keywords:
Knowledge Representation and Reasoning: Diagnosis and Abductive Reasoning
Knowledge Representation and Reasoning: Qualitative Reasoning