MUREM: A Multiplicative Regression Method for Software Development Effort Estimation

Authors

  • Ma. del Refugio Ofelia Luna Sandoval CENIDET, Departamento de Ciencias Computacionales, Cuernavaca
  • José Ruiz Ascencio Centro Nacional de Investigación y Desarrollo Tecnológico

DOI:

https://doi.org/10.13053/cys-20-4-2378

Keywords:

Software development effort estimation, method for estimating software development effort, estimating method, multiplicative method, regression model.

Abstract

In this paper a multiplicative regression method to estimate software development effort is presented. This method, which we call MUREM, is a result of, on the one hand, a set of initial conditions to frame the process of estimating software development effort and, on the other hand, a set of restrictions to be satisfied by the development effort as a function of software size. To evaluate the performance of MUREM, it was compared with three regression models which are considered as important methods for estimating software development effort. In this comparison a battery of hypothesis and standard statistical tests is applied to twelve samples taken from well-known public databases.These databases serve as benchmarks for comparing methods to estimate the software development effort. In the experimentation it was found that MUREM generates more accurate point estimates of the development effort than those achieved by the other methods. MUREM corrects the heteroscedasticity and increases the proportion of samples whose residuals show normality. MUREM thus generates more appropriate confidence and prediction intervals than those obtained by the other methods. An important result is that residuals obtained by the regression model of MUREM satisfy the test for zero mean additive white gaussian noise which is proof that the estimation error of this model is random.

Author Biographies

Ma. del Refugio Ofelia Luna Sandoval, CENIDET, Departamento de Ciencias Computacionales, Cuernavaca

Ma. del Refugio Ofelia Luna Sandoval receiveda M.Sc. degree in Computer Science from theCentro Nacional de Investigación y DesarrolloTecnológico (CENIDET), México, in 2011. She iscurrently a doctoral student at the CENIDET andher current research interests are applications ofcomputational intelligence techniques to SoftwareEngineering.

José Ruiz Ascencio, Centro Nacional de Investigación y Desarrollo Tecnológico

José Ruiz Ascencio received the B. Sc. degree in physics from (UNAM) in 1971, a M.Sc. degree from Stanford in 1973 and the D. Phil. degree from the University of Sussex in 1989. He was researcher at the Institute of Applied Mathematics, IIMASUNAM, full-time lecturer at the Autonomous University of Barcelona, automation project leader for Allen-Bradley, researcher at the InstitutoTecnológico de Monterrey, and invited scholar at McGill University’s Center for Intelligent Machines (2003 and 2010). He joined CENIDET in 1995, where he is a member of the Artificial Intelligence Group. His current interests are machine vision and computational intelligence.

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Published

2016-12-18

Issue

Vol. 20 No. 4 (2016): Thematic Issue: Research Advances and Applications of Evolutionary Computation (Guest Editors: H.J. Escalante, M. Graff)

Section

Articles

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