Abstract
Our supportive method helps improve accuracy of software effort estimation, using results of classification of Use Cases. They are classified using machine-learning method called Naïve Bayes Classifier. The result of this classification helps determine the risk of underestimation of tasks in future work on the software project.
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Acknowledgment
Work is partially supported by Grant of SP2015/85—Knowledge modeling and its applications in software engineering, VŠB - Technical University of Ostrava, Czech Republic.
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Štrba, R., Briš, R., Vondrák, I., Štolfa, S. (2016). Application of Naïve Bayes in Classification of Use Cases. In: Abraham, A., Wegrzyn-Wolska, K., Hassanien, A., Snasel, V., Alimi, A. (eds) Proceedings of the Second International Afro-European Conference for Industrial Advancement AECIA 2015. Advances in Intelligent Systems and Computing, vol 427. Springer, Cham. https://doi.org/10.1007/978-3-319-29504-6_35
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DOI: https://doi.org/10.1007/978-3-319-29504-6_35
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