The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Experience with the accuracy of software maintenance task effort prediction models
Abstract
The paper reports experience from the development and use of eleven different software maintenance effort prediction models. The models were developed applying regression analysis, neural networks and pattern recognition and the prediction accuracy was measured and compared for each model type. The most accurate predictions were achieved applying models based on multiple regression and on pattern recognition. We suggest the use of prediction models as instruments to support the expert estimates and to analyse the impact of the maintenance variables on the maintenance process and product. We believe that the pattern recognition based models evaluated, i.e., the prediction models based on the Optimized Set Reduction method, show potential for such use.>
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Cites
A leisurely look at the bootstrap, the jackknife, and cross-validation
Efron, B., & Gong, G. (1983) · The American Statistician · doi:10.1080/00031305.1983.10483087 · Cited by 3,229
Cited by
Empirical prediction models for adaptive resource provisioning in the cloud
Islam, S., Keung, J., Lee, K., & Liu, A. (2012) · Future Generation Computer Systems · doi:10.1016/j.future.2011.05.027 · Cited by 631
Metadata
- Authors
- Jorgensen, M.
- Venue
- IEEE Transactions on Software Engineering
- Published
- 1995
- Volume
- 21
- Issue
- 8
- Pages
- 674-681
- ISSN
- 0098-5589 (ISSN-L)
- OpenAlex
- W2146651617
- Cited by
- 288
- References
- 1
- FWCI
- 16.99
- Type
- journal article
- Language
- en
- Open access
- closed
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- Jorgensen, M. (1995). Experience with the accuracy of software maintenance task effort prediction models. IEEE Transactions on Software Engineering. https://doi.org/10.1109/32.403791
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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.
