Comprobación de la Adecuación de Modelos de Estimación del Esfuerzo de Desarrollo de Software Personal Basados en Lógica Difusa: Un Experimento Replicado

Cuauhtémoc López Martín¹, Cornelio Yáñez Márquez², Agustín Gutiérrez Tornés³ and Edgardo Felipe Riverón⁴

^1,2,4 Center for Computing Research, National Polytechnic Institute; P.O. 07738, México, D.F., E–mails: cuauhtemoc@sagitario.cic.ipn.mx, cyanez@cic.ipn.mx, edgardo@cic.ipn.mx

³ Systems Coordinator, Banamex, México, D.F.; ITESM, México, D.F., E–mail: agustin.tornes@itesm.mx

Article received on March 09, 2006 ]]> Accepted on July 10, 2007

Abstract

There are two main stages for using an estimation model (1) it must be determined whether the model is adequate to describe the observed (actual) data, that is, the model adequacy checking or verification; if it resulted adequate then (2) the estimation model is validated in its environment using new data. This paper is related to the first step. An investigation aimed to compare personal Fuzzy Logic Systems (FLS) with linear regression is presented. These FLS are derived from a replicated experiment using a sample integrated by ten developers. This experiment is based on both a common process and inside of a controlled environment. In six of ten cases the multiple range tests for Magnitude of Relative Error (MRE) by technique show that fuzzy logic is slightly better than linear regression. These results show that a FLS could be use as an alternative for the software development effort estimation at personal level.

Keywords: Software development effort estimation, Fuzzy logic, Linear Regression, Magnitude of Relative Error.

Resumen

Existen dos fases principales en el uso de un modelo de estimación: (1) se debe determinar si el modelo es adecuado para describir los datos observados (reales), eso es, la comprobación de la adecuación del modelo o verificación del mismo; si éste resultara adecuado, entonces (2) el modelo de estimación se valida en su ambiente usando datos nuevos. Este artículo está relacionado con la primera etapa. Se presenta una investigación dirigida a la comparación de Sistemas de Lógica Difusa (SLD) personales. Estos SLD se derivan a partir de un experimento replicado con base en una muestra de diez desarrolladores, así como en un proceso de desarrollo común dentro de un entorno controlado. En seis de los diez casos, las pruebas de rango múltiple de la Magnitud del Error Relativo (MER) por técnica, muestran que la lógica difusa es ligeramente mejor que la regresión simple. Estos resultados muestran que un SLD podría ser utilizado como alternativa para la estimación del esfuerzo de desarrollo de software a nivel personal.

Palabras clave: Estimación del esfuerzo de desarrollo de software, Lógica difusa, Regresión lineal, Magnitud del error relativo.

Acknowledgements

We would like to thank Center for Computing Research, National Polytechnic Institute, Mexico as well as CONACYT. Moreover, to Development Team which is working for Federal Commission of Electricity at Guadalajara, Jalisco, México as well as the bachelor students of Computational Systems Engineering of the University del Valle de Atemajac (UNIVA), Guadalajara.

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Appendix

The following lists include the identifiers, names, programming language, and their job/university of developers:

a) Development Team, Federal Commission of Electricity from Guadalajara, Director's email: omar.delacruz@cfe.gob.mx. A: (Alatorre Carranza N., C) B: (De la Cruz Preciado O., Pascal); C: (Flores Gómez C., COBOL); D: (Galindo Gauna R., C); E: (García Ramos M., C); F: (Guerra Martínez A., Pascal); G: (Guzmán Martínez A., C); H: ( Hernández Hernández P., COBOL) I: (Hernández Ramos A., COBOL); J: (Partida Menchuca L., COBOL).

b) Bachelor Students, University del Valle de Atemajac (UNIVA), Guadalajara, http://www.univa.mx/, Director's email: martin.rodriguez@univa.mx. K: (Becerril Ramírez J., Delphi); L: (Herrera Rábago F., Pascal); M: (Navarro Rodríguez J., C); N: (Santana Ruelas J., C) O: (Vargas Mora D., JAVA).