Baital: An Adaptive Weighted Sampling Approach for Improved t-wise Coverage
(2020) 28th ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering — Location: Virtual Event, USA (8.November.2020)
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- Abstract
- The rise of highly configurable complex software and its widespread usage requires design of efficient testing methodology. t-wise coverage is a leading metric to measure the quality of the testing suite and the underlying test generation engine. While uniform sampling-based test generation is widely believed to be the state of the art approach to achieve t-wise coverage in presence of constraints on the set of configurations, such a scheme often fails to achieve high t-wise coverage in presence of complex constraints. In this work, we propose a novel approach Baital, based on adaptive weighted sampling using literal weighted functions, to generate test sets with high t-wise coverage. We demonstrate that our approach reaches significantly higher t-wise coverage than uniform sampling. The novel usage of literal weighted sampling leaves open several interesting directions, empirical as well as theoretical, for future research.
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Citations
Baranov, E., Legay, A., & Meel, K. S. (2020). Baital: An Adaptive Weighted Sampling Approach for Improved t-wise Coverage. ESEC/FSE 2020: Proceedings of the 28th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Founda. Published. 28th ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering, Virtual Event, USA. https://doi.org/10.1145/3368089.3409744