The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.
Local versus Global Lessons for Defect Prediction and Effort Estimation
Abstract
Existing research is unclear on how to generate lessons learned for defect prediction and effort estimation. Should we seek lessons that are global to multiple projects or just local to particular projects? This paper aims to comparatively evaluate local versus global lessons learned for effort estimation and defect prediction. We applied automated clustering tools to effort and defect datasets from the PROMISE repository. Rule learners generated lessons learned from all the data, from local projects, or just from each cluster. The results indicate that the lessons learned after combining small parts of different data sources (i.e., the clusters) were superior to either generalizations formed over all the data or local lessons formed from particular projects. We conclude that when researchers attempt to draw lessons from some historical data source, they should 1) ignore any existing local divisions into multiple sources, 2) cluster across all available data, then 3) restrict the learning of lessons to the clusters from other sources that are nearest to the test data.
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Cites
Learning representations by back-propagating errors
Rumelhart, D. E., Hinton, G. E., & Williams, R. J. (1986) · Nature · doi:10.1038/323533a0 · Cited by 31,892
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Cited by
An empirical analysis of data preprocessing for machine learning-based software cost estimation
Huang, J., Li, Y.-F., & Xie, M. (2015) · Information and Software Technology · doi:10.1016/j.infsof.2015.07.004 · Cited by 223
An Empirical Study of Model-Agnostic Techniques for Defect Prediction Models
Jiarpakdee, J., Tantithamthavorn, C. K., Dam, H. K., & Grundy, J. (2022) · IEEE Transactions on Software Engineering · doi:10.1109/tse.2020.2982385 · Cited by 148
Design Rule Spaces: A New Model for Representing and Analyzing Software Architecture
Cai, Y., Xiao, L., Kazman, R., Mo, R., & Feng, Q. (2019) · IEEE Transactions on Software Engineering · doi:10.1109/tse.2018.2797899 · Cited by 39
Metadata
- Authors
- Menzies, T., Butcher, A., Cok, D., Marcus, A., Layman, L., Shull, F., Turhan, B., & Zimmermann, T.
- Venue
- IEEE Transactions on Software Engineering
- Published
- 2013-06
- Volume
- 39
- Issue
- 6
- Pages
- 822-834
- ISSN
- 0098-5589 (ISSN-L)
- 1939-3520
- OpenAlex
- W1989354793
- Cited by
- 261
- References
- 3
- FWCI
- 78.95
- Type
- journal article
- Language
- en
- Open access
- closed
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- Menzies, T., Butcher, A., Cok, D., Marcus, A., Layman, L., Shull, F., Turhan, B., & Zimmermann, T. (2013). Local versus Global Lessons for Defect Prediction and Effort Estimation. IEEE Transactions on Software Engineering. https://doi.org/10.1109/tse.2012.83
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