The DOI matches its Crossref record.
Analysis of faults in an N-version software experiment
Abstract
The authors have conducted a large-scale experiment in N-version programming. A total of 27 versions of a program were prepared independently from the same specification at two universities. The results of executing the versions revealed that the versions were individually extremely reliable but that the number of input cases in which more than one failed was substantially more than would be expected if they were statistically independent. After the versions had been executed, the failures of each version were examined and the associated faults located. It appears that minor differences in the software development environment would not have a major impact in reducing the incidence of faults that cause correlated failures.>
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Citations
Cited by
Using simplicity to control complexity
Sha, L. (2001) · IEEE Software · doi:10.1109/ms.2001.936213 · Cited by 420
Testing scientific software: A systematic literature review
Kanewala, U., & Bieman, J. M. (2014) · Information and Software Technology · doi:10.1016/j.infsof.2014.05.006 · Cited by 150
Metadata
- Authors
- Brilliant, S. S., Knight, J. C., & Leveson, N. G.
- Venue
- IEEE Transactions on Software Engineering
- Published
- 1990
- Volume
- 16
- Issue
- 2
- Pages
- 238-247
- ISSN
- 0098-5589 (ISSN-L)
- OpenAlex
- W2126870663
- Cited by
- 165
- References
- 0
- FWCI
- 21.31
- Type
- journal article
- Language
- en
- Open access
- closed
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- CoChat. (2026). Citation check: Analysis of faults in an N-version software experiment. https://app.cochat.ai/papers/10.1109/32.44387
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- Brilliant, S. S., Knight, J. C., & Leveson, N. G. (1990). Analysis of faults in an N-version software experiment. IEEE Transactions on Software Engineering. https://doi.org/10.1109/32.44387
Data sources
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.
