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A model for spectra-based software diagnosis
Abstract
This article presents an improved approach to assist diagnosis of failures in software (fault localisation) by ranking program statements or blocks in accordance with to how likely they are to be buggy. We present a very simple single-bug program to model the problem. By examining different possible execution paths through this model program over a number of test cases, the effectiveness of different proposed spectral ranking methods can be evaluated in idealised conditions. The results are remarkably consistent to those arrived at empirically using the Siemens test suite and Space benchmarks. The model also helps identify groups of metrics that are equivalent for ranking. Due to the simplicity of the model, an optimal ranking method can be devised. This new method out-performs previously proposed methods for the model program, the Siemens test suite and Space. It also helps provide insight into other ranking methods.
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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
Evaluating and Improving Fault Localization
Pearson, S., Campos, J., Just, R., Fraser, G., Abreu, R., Ernst, M. D., Pang, D., & Keller, B. (2017) · 2017 IEEE/ACM 39th International Conference on Software Engineering (ICSE) · doi:10.1109/icse.2017.62 · Cited by 426
A theoretical analysis of the risk evaluation formulas for spectrum-based fault localization
Xie, X., Chen, T. Y., Kuo, F.-C., & Xu, B. (2013) · ACM Transactions on Software Engineering and Methodology · doi:10.1145/2522920.2522924 · Cited by 353
DeepFL: integrating multiple fault diagnosis dimensions for deep fault localization
Li, X., Li, W., Zhang, Y., & Zhang, L. (2019) · Proceedings of the 28th ACM SIGSOFT International Symposium on Software Testing and Analysis · doi:10.1145/3293882.3330574 · Cited by 336
Zhou, X., Peng, X., Xie, T., Sun, J., Ji, C., Liu, D., Xiang, Q., & He, C. (2019) · Proceedings of the 2019 27th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering · doi:10.1145/3338906.3338961 · Cited by 277
Metadata
- Authors
- Naish, L., Lee, H. J., & Ramamohanarao, K.
- Venue
- ACM Transactions on Software Engineering and Methodology
- Published
- 2011-08
- Volume
- 20
- Issue
- 3
- Pages
- 1-32
- ISSN
- 1049-331X (ISSN-L)
- 1557-7392
- OpenAlex
- W2010833880
- Cited by
- 536
- References
- 0
- FWCI
- 77.02
- Type
- journal article
- Language
- en
- Open access
- closed
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- Naish, L., Lee, H. J., & Ramamohanarao, K. (2011). A model for spectra-based software diagnosis. ACM Transactions on Software Engineering and Methodology. https://doi.org/10.1145/2000791.2000795
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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.
