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Algorithmic optimization method for effort estimation

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This paper presents an improvement of an effort estimation method that can be used to predict the level of effort for software development projects. A new estimation approach based on a two-phase algorithm is used. In the first phase, we apply a calculation based on use case points (UCPs). In the second phase, we add correction values (a 1, a 2) obtained via least squares regression. This approach employs historical project data to refine the estimate. By applying the least squares regression approach, the algorithm filters out estimation errors caused by human factors and company practice.

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  1. Robiolo, G. and Orosco, R., Employing use cases to early estimate effort with simpler metrics, Innovations Syst. Software Eng., 2008, no. 1, pp. 31–43.

    Article  Google Scholar 

  2. Karner, G., Metrics for objectory, Diploma, University of Linkoping, 1993.

    Google Scholar 

  3. Braz, M.R. and Vergilio, S.R., Software effort estimation based on use cases, Proc. 30th Annu. Int. Computer Software and Applications Conf. (COMPSAC), 2006.

    Google Scholar 

  4. Wang, F., et al., Extended use case points method for software cost estimation, 2009, pp. 1–5.

    Google Scholar 

  5. Diev, S., Use cases modeling and software estimation, ACM SIGSOFT Software Eng. Notes, 2006, vol. 31, no. 6, p. 1.

    Article  Google Scholar 

  6. Mohagheghi, P., Anda, B., and Conradi, R., Effort estimation of use cases for incremental large-scale software development, 2005, pp. 303–311.

    Google Scholar 

  7. Robiolo, G., Badano, C., and Orosco, R., Transactions and paths: Two use case based metrics which improve the early effort estimation, 2009, pp. 422–425.

    Google Scholar 

  8. Azevedo, S., et al., On the refinement of use case models with variability support, Innovations Syst. Software Eng., 2011, vol. 8, no. 1, pp. 51–64.

    Article  Google Scholar 

  9. Ochodek, M., Nawrocki, J., and Kwarciak, K., Simplifying effort estimation based on use case points, Inf. Software Technol., 2011, vol. 53, no. 3, pp. 200–213.

    Article  Google Scholar 

  10. Silhavy, R., Silhavy, P., and Prokopova, Z., Algorithmic optimisation method for improving use case points estimation, PLoS One, 2015.

    Google Scholar 

  11. Ochodek, M., et al., Improving the reliability of transaction identification in use cases, Inf. Software Technol., 2011, vol. 53, no. 8, pp. 885–897.

    Article  Google Scholar 

  12. Pindyck, R.S. and Rubinfeld, D.L., Econometric Models and Economic Forecasts, Boston: Irwin/McGraw-Hill, 1998.

    Google Scholar 

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Correspondence to R. Silhavy.

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Original Russian Text © R. Silhavy, Z. Prokopova, P. Silhavy, 2016, published in Programmirovanie, 2016, Vol. 42, No. 3.

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Silhavy, R., Prokopova, Z. & Silhavy, P. Algorithmic optimization method for effort estimation. Program Comput Soft 42, 161–166 (2016).

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