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Finding failures by cluster analysis of execution profiles
Abstract
We experimentally evaluate the effectiveness of using cluster analysis of execution profiles to find failures among the executions induced by a set of potential test cases. We compare several filtering procedures for selecting executions to evaluate for conformance to requirements. Each filtering procedure involves a choice of a sampling strategy and a clustering metric. The results suggest that filtering procedures based on clustering are more effective than simple random sampling for identifying failures in populations of operational executions, with adaptive sampling from clusters being the most effective sampling strategy. The results also suggest that clustering metrics that give extra weight to unusual profile features are most effective. Scatter plots of execution populations, produced by multidimensional scaling, are used to provide intuition for these results. Keywords: Observation-based testing, software testing, operational testing, beta testing, cluster analysis, multidimensional scaling 1.
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Cited by
A Survey on Software Fault Localization
Wong, W. E., Gao, R., Li, Y., Abreu, R., & Wotawa, F. (2016) · IEEE Transactions on Software Engineering · doi:10.1109/tse.2016.2521368 · Cited by 1,110
Brubaker, C., Jana, S., Ray, B., Khurshid, S., & Shmatikov, V. (2014) · 2014 IEEE Symposium on Security and Privacy · doi:10.1109/sp.2014.15 · Cited by 218
Metadata
- Authors
- Dickinson, W. T., De, L., & Fodgurski, A.
- Published
- 2001-07-01
- Pages
- 339-348
- DOI
- 10.5555/381473.381509
- OpenAlex
- W2156618683
- Cited by
- 252
- References
- 0
- FWCI
- 22.55
- Type
- journal article
- Language
- en
- Open access
- closed
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
