The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.
Developing interpretable models with optimized set reduction for identifying high-risk software components
Open accessAbstract
Applying equal testing and verification effort to all parts of a software system is not very efficient, especially when resources are tight. Therefore, one needs to low/high fault frequency components so that testing/verification effort can be concentrated where needed. Such a strategy is expected to detect more faults and thus improve the resulting reliability of the overall system. The authors present the optimized set reduction approach for constructing such models, which is intended to fulfill specific software engineering needs. The approach to classification is to measure the software system and build multivariate stochastic models for predicting high-risk system components. Experimental results obtained by classifying Ada components into two classes (is, or is not likely to generate faults during system and acceptance rest) are presented. The accuracy of the model and the insights it provides into the error-making process are evaluated.>
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Cites
Quinlan, J. R. (1986) · Machine Learning · doi:10.1007/bf00116251 · Cited by 21,439
Hosmer, D. W., Lemeshow, S., & Sturdivant, R. X. (2013) · Wiley Series in Probability and Statistics · doi:10.1002/9781118548387 · Cited by 13,861
Agresti, A. (2002) · Wiley Series in Probability and Statistics · doi:10.1002/0471249688 · Cited by 6,746
Cited by
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Lakkaraju, H., Bach, S. H., & Leskovec, J. (2016) · Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining · doi:10.1145/2939672.2939874 · Cited by 687
The confounding effect of class size on the validity of object-oriented metrics
El Emam, K., Benlarbi, S., Goel, N., & Rai, S. N. (2001) · IEEE Transactions on Software Engineering · doi:10.1109/32.935855 · Cited by 422
Bennin, K. E., Keung, J., Phannachitta, P., Monden, A., & Mensah, S. (2018) · IEEE Transactions on Software Engineering · doi:10.1109/tse.2017.2731766 · Cited by 298
Experience with the accuracy of software maintenance task effort prediction models
Jorgensen, M. (1995) · IEEE Transactions on Software Engineering · doi:10.1109/32.403791 · Cited by 288
Robust Prediction of Fault-Proneness by Random Forests
Guo, L., Ma, Y., Cukic, B., & Singh, H. (2005) · 15th International Symposium on Software Reliability Engineering · doi:10.1109/issre.2004.35 · Cited by 271
Metadata
- Authors
- Briand, L. C., Brasili, V. R., & Hetmanski, C. J.
- Venue
- IEEE Transactions on Software Engineering
- Published
- 1993
- Volume
- 19
- Issue
- 11
- Pages
- 1028-1044
- ISSN
- 0098-5589 (ISSN-L)
- OpenAlex
- W2133711389
- Cited by
- 179
- References
- 3
- FWCI
- 21.04
- Type
- journal article
- Language
- en
- Open access
- green
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- Briand, L. C., Brasili, V. R., & Hetmanski, C. J. (1993). Developing interpretable models with optimized set reduction for identifying high-risk software components. IEEE Transactions on Software Engineering. https://doi.org/10.1109/32.256851
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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