The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
On the value of static analysis for fault detection in software
Abstract
No single software fault-detection technique is capable of addressing all fault-detection concerns. Similarly to software reviews and testing, static analysis tools (or automated static analysis) can be used to remove defects prior to release of a software product. To determine to what extent automated static analysis can help in the economic production of a high-quality product, we have analyzed static analysis faults and test and customer-reported failures for three large-scale industrial software systems developed at Nortel Networks. The data indicate that automated static analysis is an affordable means of software fault detection. Using the orthogonal defect classification scheme, we found that automated static analysis is effective at identifying assignment and checking faults, allowing the later software production phases to focus on more complex, functional, and algorithmic faults. A majority of the defects found by automated static analysis appear to be produced by a few key types of programmer errors and some of these types have the potential to cause security vulnerabilities. Statistical analysis results indicate the number of automated static analysis faults can be effective for identifying problem modules. Our results indicate static analysis tools are complementary to other fault-detection techniques for the economic production of a high-quality software product.
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Cites
Orthogonal defect classification-a concept for in-process measurements
Chillarege, R., Bhandari, I. S., Chaar, J. K., Halliday, M. J., Moebus, D. S., Ray, B. K., & Wong, M.-Y. (1992) · IEEE Transactions on Software Engineering · doi:10.1109/32.177364 · Cited by 792
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
Predicting Vulnerable Software Components via Text Mining
Scandariato, R., Walden, J., Hovsepyan, A., & Joosen, W. (2014) · IEEE Transactions on Software Engineering · doi:10.1109/tse.2014.2340398 · Cited by 337
A Taxonomy of Integration-Relevant Faults for Microservice Testing
Gregor, L., Hentschel, A., Kastner, L., & Pretschner, A. (2025) · 2025 IEEE Conference on Software Testing, Verification and Validation (ICST) · doi:10.1109/icst62969.2025.10989000 · Cited by 5
Metadata
- Authors
- Zheng, J., Williams, L., Nagappan, N., Snipes, W., Hudepohl, J. P., & Vouk, M. A.
- Venue
- IEEE Transactions on Software Engineering
- Published
- 2006-04
- Volume
- 32
- Issue
- 4
- Pages
- 240-253
- ISSN
- 0098-5589 (ISSN-L)
- OpenAlex
- W2107024044
- Cited by
- 283
- References
- 1
- FWCI
- 34.76
- Type
- journal article
- Language
- en
- Open access
- closed
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- Zheng, J., Williams, L., Nagappan, N., Snipes, W., Hudepohl, J. P., & Vouk, M. A. (2006). On the value of static analysis for fault detection in software. IEEE Transactions on Software Engineering. https://doi.org/10.1109/tse.2006.38
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