Abstract
This study proposed a stepwise regression clustering method for software development effort estimation. The proposed algorithm is based on functional points analysis and is used for forming clusters, which contains analogical projects. Furthermore, it is expected that clusters will be shaped well for the regression prediction models. The proposed models are based on Cook distance, which is used for elimination project from clusters. Model performance is proved for selected clusters. Overall model performance influenced by selected clusters, therefore, there is no statistically significant difference between regression models based on clustered and un-clustered datasets.
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References
Silhavy, R., Silhavy, P., Prokopova, Z.: Analysis and selection of a regression model for the use case points method using a stepwise approach. J. Syst. Softw. 125, 1–14 (2017)
Silhavy, R., Silhavy, P., Prokopova, Z.: Evaluating subset selection methods for use case points estimation. Inf. Softw. Technol. 97, 1–9 (2018)
Azzeh, M., Nassif, A.B., Banitaan, S.: Comparative analysis of soft computing techniques for predicting software effort based use case points. IET Softw. 12(1), 19–29 (2018)
Abualkishik, A.Z., Lavazza, L.: IFPUG function points to COSMIC function points convertibility: a fine-grained statistical approach. Inf. Softw. Technol. 97, 179–191 (2018)
Abualkishik, A.Z., et al.: A study on the statistical convertibility of IFPUG function point, COSMIC function point and simple function point. Inf. Softw. Technol. 86, 1–19 (2017)
Albrecht, A.J.: Measuring application development productivity. In: Proceedings of IBM Application Development Joint SHARE/GUIDE Symposium, Monterey, CA, 1979 (1979)
Idri, A., Amazal, F.A., Abran, A.: Analogy-based software development effort estimation: a systematic mapping and review. Inf. Softw. Technol. 58, 206–230 (2015)
Lokan, C., Mendes, E.: Investigating the use of duration-based moving windows to improve software effort prediction. In: 2012 19th Asia-Pacific Software Engineering Conference (Apsec), vol. 1, pp. 818–827 (2012)
Silhavy, P., Silhavy, R., Prokopova, Z.: Evaluation of data clustering for stepwise linear regression on use case points estimation. In: Advances in Intelligent Systems and Computing, pp. 491–496 (2017)
Silhavy, P., Silhavy, R., Prokopova, Z.: Evaluation of data clustering for stepwise linear regression on use case points estimation. In: Computer Science On-line Conference, Springer, Cham (2017)
ISBSG: ISBSG development and enhancement repository – release 13. International Software Benchmarking Standards Group (ISBSG) (2015)
Stevens, J.P.: Outliers and influential data points in regression analysis. Psychol. Bull. 95(2), 334 (1984)
Jayakumar, D.S., Sulthan, A.: A new procedure of regression clustering based on Cook’s D. Electron. J. Appl. Stat. Anal. 8, 13–27 (2015)
de Myttenaere, A., et al.: Mean absolute percentage error for regression models. Neurocomputing 192(Suppl. C), 38–48 (2016)
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Silhavy, P., Silhavy, R., Prokopova, Z. (2019). Stepwise Regression Clustering Method in Function Points Estimation. In: Silhavy, R., Silhavy, P., Prokopova, Z. (eds) Computational and Statistical Methods in Intelligent Systems. CoMeSySo 2018. Advances in Intelligent Systems and Computing, vol 859. Springer, Cham. https://doi.org/10.1007/978-3-030-00211-4_29
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DOI: https://doi.org/10.1007/978-3-030-00211-4_29
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