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Multi-stream online transfer learning for software effort estimation: is it necessary?
2021
Proceedings of the 17th International Conference on Predictive Models and Data Analytics in Software Engineering
Software Effort Estimation (SEE) may suffer from changes in the relationship between features describing software projects and their required effort over time, hindering predictive performance of machine learning models. To cope with that, most machine learning-based SEE approaches rely on receiving a large number of Within-Company (WC) projects for training over time, being prohibitively expensive. The approach Dycom reduces the number of required WC training projects by transferring knowledge
doi:10.1145/3475960.3475988
fatcat:gqdhmmqqubdppcs4raqtwgja5q