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Applying a general regression neural network for predicting development effort of short-scale programs

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Abstract

Software development effort prediction is considered in several international software processes as the Capability Maturity Model-Integrated (CMMi), by ISO-15504 as well as by ISO/IEC 12207. In this paper, data of two kinds of lines of code gathered from programs developed with practices based on the Personal Software Process (PSP) were used as independent variables in three models for estimating and predicting the development effort. Samples of 163 and 80 programs were used for verifying and validating, respectively, the models. The prediction accuracy comparison among a multiple linear regression, a general regression neural network, and a fuzzy logic model was made using as criteria the magnitude of error relative to the estimate (MER) and mean square error (MSE). Results accepted the following hypothesis: effort prediction accuracy of a general regression neural network is statistically equal than those obtained by a fuzzy logic model as well as by a multiple linear regression, when new and change code and reused code obtained from short-scale programs developed with personal practices are used as independent variables.

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Acknowledgments

Author of this paper would like to thank CUCEA of Guadalajara University, Jalisco, México, Programa de Mejoramiento del Profesorado (PROMEP), as well as to Consejo Nacional de Ciencia y Tecnología (Conacyt).

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Authors and Affiliations

  1. Information Systems Department, CUCEA, Guadalajara University, P.O. Box 45100, Guadalajara, Jalisco, Mexico

    Cuauhtemoc Lopez-Martin

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  1. Cuauhtemoc Lopez-Martin
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Correspondence to Cuauhtemoc Lopez-Martin.

Appendices

Appendix 1

Actual data by developer from design to testing phases for generating and verifying the models: P: Number of program, DP: Developer (see Appendix 3), N&C: New and Changed code, R: Reused code, AE: Actual Effort (minutes), MER: Magnitude of Error Relative to the estimate, MLR: Multiple Linear Regression, GRNN: General regression neural network; FLM: Fuzzy Logic Model (Table 6).

Table 6  
Full size table

Appendix 2

Actual data by developer from design to testing phases for validating the models: P: Number of program, DP: Developer (Appendix 3), N&C: New and Changed code, R: Reused code, AE: Actual Effort (minutes), MER: Magnitude of Error Relative to the estimate, MLR: Multiple Linear Regression, GRNN: General regression neural network; FLM: Fuzzy Logic Model (Table 7).

Table 7  
Full size table

Appendix 3

Names of developers from Federal Commission of Electricity (Cn) at Guadalajara, Jalisco (Director email: omar.delacruz@cfe.gob.mx); CINVESTAV (Pn) at Guadalajara, Jalisco (Director email: jesus.vazquez@cts-design.com); Universidad del Valle de Atemajac (Un) at Guadalajara, Jalisco (Director email: elena.gonzalez@univa.mx); Guadalajara University (Mn) at Jalisco (Director email: leonardo.soto@cucea.udg.mx); Universidad del Valle de Atemajac (UMn) at Leon, Guanajuato (Director email: luis.garcia@univa.mx).

Verification stage: C1: Barraza A. I., C2: De la Cruz P. O., C3: Flores G. C., C4: Galindo G. R., C5: García R. M., C6: Guerra M. A., C7: Guzmán M. A., C8: Hernández H. P., C9: Hernández R. A., C10: Partida M. L., P1: Alegría B. J., P2: Escamilla R. J., P3: Gutíerrez R. F., P4: Montesinos S. J., P5: Morales L. D., P6: Plascencia S. J., P7: Reynoso R. R., P8: Rivera V. B., P9: Vega B. F., P10: Viramontes C. A., P11: Cordero B. D., P12: Davis A. R., P13: Díaz I. M. J., P14: Domínguez Z. S., P15: Duarte L. M., P16: Jiménez G. N., P17: Montero S.A., P18: Martínez S. N., P19: Rocha H. J., P20: Vega Á. C., P21: González C. D., P22: Gutiérrez R. L., P23: Muñetón P. O., P24: Plata V. P., P25: Tapia G. S., P26: Aguirre Z. M., P27: Calvillo C. C., P28: Gallegos R. L., P29: Hernández O. O., P30: Meza A. E., P31: Ramos C. L., P32: Sapiens P. J., U1: Gutiérrez H. A., U2: Tamayo E., U3: Ayala A. C., U4: Gonzalez Q. R., U5: Navarro N. S., U6: Rivera P. E., U7: Zavala G. H., U8: Cardosa M. T., U9: Cortés F. G., U10: Lugo R. J., U11: Martínez G. O.

Validation stage: M1: Cabral J.J., M2: Dueñas del Toro H. O., M3: González P.J.J., M4: Herrera B. K., M5: Lopez F. A. D., M6: Maciel A. L. A., M7: Moreno G. M., M8: Ramos C. S., M9: Vallejo M. E., M10: Villegas R. M., M11: Carrillo D. I., M12: Castro T. M., M13: Estrada V. L., M14: Garcia V. L., M15: Herrera I. J., M16: Mercado G. S., M17: Peñalba V. A., M18: Ramírez L. E., M19: Robledo H. A., M20: Torres A. U., M21: Torres E. A., UM1: Castillo O. R., UM2: Cedillo B. F., UM3: Cruz G. A., UM4: Martínez P. R.,UM5: Padilla H. B., UM6: Palomares A. L., UM7: Ramirez R. M., UM8: Rodríguez S. J., UM9: Zuñiga A. V.

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Lopez-Martin, C. Applying a general regression neural network for predicting development effort of short-scale programs. Neural Comput & Applic 20, 389–401 (2011). https://doi.org/10.1007/s00521-010-0405-5

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