Towards ensemble-based use case point prediction
Author:
Funder
Ministry of Education, India
Publisher
Springer Science and Business Media LLC
Subject
Safety, Risk, Reliability and Quality,Software
Link
https://link.springer.com/content/pdf/10.1007/s11219-022-09612-2.pdf
Reference46 articles.
1. Ali, A., & Gravino, C. (2019). A systematic literature review of software effort prediction using machine learning methods. Journal of Software: Evolution and Process.
2. Azzeh, M., Nassif, A., Banitaan, S., & Martín, C. (2018). Ensemble of learning project productivity in software effort based on use case points. In 17th IEEE International Conference on Machine Learning and Applications (ICMLA), pp. 1427-1431.
3. Azzeh, M., Nassif, A., & Martín C. (2021b). Empirical analysis on productivity prediction and locality for use case points method. Software Quality Journal, 29(2), 309–336.
4. Azzeh, M., Nassif, A. B., & Attili, I. B. (2021a). Predicting software effort from use case points: A systematic review. Science of Computer Programming, 204, 102596.
5. Badri, M., Badri, L., Flageol, W., & Toure, F. (2017). Source code size prediction using use case metrics: an empirical comparison with use case points. Innovations in Systems and Software Engineering, 13(2), 143–159.
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