Abstract
Many decisions that are needed for the planning of the software development project are based on previous experience and competency of project manager. One of the most important questions is how much effort will be necessary to complete the task. In our case, the task is described by the use case and manger has to estimate the effort to implement it. However, such estimations are not always correct, not estimated extra work has to be done sometimes. Our intent is to support manager’s decision by the estimation tool that uses know parameters of the use cases to predict other parameters that has to be estimated. This paper focuses on the usage of our method on the real data and evaluates its results in real development. The method uses parameterized use case model trained from the previously done use cases to predict extra work parameter. Estimation of test use cases is done several times according to the managers needs during the project execution.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Crestani, F., Pasi, G.: Soft information retrieval: Applications of fuzzy set theory and neural networks. In: Kasabov, N., Kozma, R. (eds.) Neuro-Fuzzy Techniques for Intelligent Information Systems, pp. 287–315. Springer, Heidelberg (1999)
Klir, G.J., Yuan, B.: Fuzzy Sets and Fuzzy Logic. Theory and Applications. Prentice Hall, Upper Saddle River (1995)
Kraft, D.H., Petry, F.E., Buckles, B.P., Sadasivan, T.: Genetic Algorithms for Query Optimization in Information Retrieval: Relevance Feedback. In: Sanchez, E., Shibata, T., Zadeh, L.A. (eds.) Genetic Algorithms and Fuzzy Logic Systems. World Scientific, Singapore (1997)
Krömer, P., Platoš, J., Snášel, V., Abraham, A., Prokop, L., Mišák, S.: Genetically evolved fuzzy predictor for photovoltaic power output estimation. In: 2011 Third International Conference on Intelligent Networking and Collaborative Systems (INCoS), pp. 41–46. IEEE (2011)
Krömer, P., Snášel, V., Platoš, J.: Learning patterns from data by an evolutionary-fuzzy approach. In: Corchado, E., Snášel, V., Sedano, J., Hassanien, A.E., Calvo, J.L., Ślęzak, D. (eds.) SOCO 2011. AISC, vol. 87, pp. 127–135. Springer, Heidelberg (2011)
Snášel, V., Krömer, P., Platoš, J., Abraham, A.: The evolution of fuzzy classifier for data mining with applications. In: Deb, K., et al. (eds.) SEAL 2010. LNCS, vol. 6457, pp. 349–358. Springer, Heidelberg (2010)
Zadeh, L.A.: Fuzzy sets. Information and Control 8, 338–353 (1965)
Kohonen, T.: The Self-Organizing Map. Proceedings of the IEEE 78(9) (September 1990)
Kohonen, T., Oja, E., Simula, O., Visa, A., Kangas, J.: Engineering Applications of the Self-Organizing Map. Proceedings of the IEEE 84(10) (October 1996)
Vesanto, J., Alhoniemi, E.: Clustering of the Self-Organizing Map. IEEE Transactions on Neural Networks 11(3) (May 2000)
Heemstra, F.J.: Software cost estimation. Information and Software Technology 34(10) (October 1992)
Boehm, B.: Software Engineering Economics. Prentice Hall (1981)
Staron, M., Meding, W.: Defect Inflow Prediction in Large Software Projects. e-Informatica Software Engineering Journal 3(1) (2009)
Ochodek, M., Nawrocki, J., Kwarciak, K.: Simplifying effort estimation based on Use Case Points. Information and Software Technology 53(3), 200–213 (2011)
Štolfa, J., Štolfa, S., Koběrský, O., Kopka, M., Kožuszník, J., Snášel, V.: Methodology for Estimating Working Time Effort of the Software Project. In: 2012 Databases, Texts, Specifications, and Objects (DATESO), pp. 25–37 (2012)
Affenzeller, M., Winkler, S., Wagner, S., Beham, A.: Genetic Algorithms and Genetic Programming: Modern Concepts and Practical Applications. Chapman & Hall/CRC (2009)
Beshah, T., Ejigu, D., Kromer, P., Snasel, V., Platos, J., Abraham, A.: Learning the classification of traffic accident types. In: 2012 4th International Conference on Intelligent Networking and Collaborative Systems (INCoS), pp. 463–468 (September 2012)
Campbell, C., Ying, Y.: Learning with support vector machines. Synthesis Lectures on Artificial Intelligence and Machine Learning 5(1), 1–95 (2011)
Feuring, T.: Fuzzy-systeme. Institut für Informatik. Westfälische Wilhelms Universität, Münster (1996)
Hamel, L.H.: Knowledge Discovery with Support Vector Machines. Wiley-Interscience, New York (2009)
Herbrich, R.: Learning Kernel Classifiers: Theory and Algorithms (Adaptive Computation and Machine Learning). The MIT Press (December 2001)
Jantzen, J.: Tutorial On Fuzzy Logic. Technical Report 98-E-868 (logic), Technical University of Denmark, Dept. of Automation (1998)
Kecman, V.: Support vector machines an introduction. In: Wang, L. (ed.) Support Vector Machines: Theory and Applications. STUDFUZZ, vol. 177, pp. 1–47. Springer, Heidelberg (2005)
Krömer, P., Platoš, J., Snášel, V., Abraham, A.: Fuzzy classification by evolutionary algorithms. In: IEEE International Conference on Systems, Man, and Cybernetics, pp. 313–318. IEEE System, Man, and Cybernetics Society (2011)
Pasi, G.: Fuzzy sets in information retrieval: State of the art and research trends. In: Bustince, H., Herrera, F., Montero, J. (eds.) Fuzzy Sets and Their Extensions: Representation, Aggregation and Models. STUDFUZZ, vol. 220, pp. 517–535. Springer, Heidelberg (2008)
Štolfa, J., Koběrský, O., Kopka, M., Krömer, P., Štolfa, S., Kožuszník, J., Snášel, V.: Value estimation of the use case parameters using SOM and fuzzy rules. In: The International ACM Conference of Emergent Digital EcoSystems, MEDES (2012)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer International Publishing Switzerland
About this paper
Cite this paper
Štolfa, S., Štolfa, J., Krömer, P., Koběrský, O., Kopka, M., Snášel, V. (2014). Fuzzy Rules and SVM Approach to the Estimation of Use Case Parameters. In: Abraham, A., Krömer, P., Snášel, V. (eds) Innovations in Bio-inspired Computing and Applications. Advances in Intelligent Systems and Computing, vol 237. Springer, Cham. https://doi.org/10.1007/978-3-319-01781-5_4
Download citation
DOI: https://doi.org/10.1007/978-3-319-01781-5_4
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-01780-8
Online ISBN: 978-3-319-01781-5
eBook Packages: EngineeringEngineering (R0)