The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
A pattern recognition approach for software engineering data analysis
Abstract
In order to plan, control, and evaluate the software development process, one needs to collect and analyze data in a meaningful way. Classical techniques for such analysis are not always well suited to software engineering data. A pattern recognition approach for analyzing software engineering data, called optimized set reduction (OSR), that addresses many of the problems associated with the usual approaches is described. Methods are discussed for using the technique for prediction, risk management, and quality evaluation. Experimental results are provided to demonstrate the effectiveness of the technique for the particular application of software cost estimation.>
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Cites
Quinlan, J. R. (1986) · Machine Learning · doi:10.1007/bf00116251 · Cited by 21,439
Cited by
Systematic literature review of machine learning based software development effort estimation models
Wen, J., Li, S., Lin, Z., Hu, Y., & Huang, C. (2012) · Information and Software Technology · doi:10.1016/j.infsof.2011.09.002 · Cited by 526
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
Metadata
- Authors
- Briand, L. C., Basili, V. R., & Thomas, W. M.
- Venue
- IEEE Transactions on Software Engineering
- Published
- 1992
- Volume
- 18
- Issue
- 11
- Pages
- 931-942
- ISSN
- 0098-5589 (ISSN-L)
- OpenAlex
- W2098168647
- Cited by
- 239
- References
- 1
- FWCI
- 17.43
- Type
- journal article
- Language
- en
- Open access
- closed
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- Briand, L. C., Basili, V. R., & Thomas, W. M. (1992). A pattern recognition approach for software engineering data analysis. IEEE Transactions on Software Engineering. https://doi.org/10.1109/32.177363
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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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