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

Authors:
Cuauhtemoc Lopez-Martin
Affiliations:
Guadalajara University, Information Systems Department, CUCEA, P.O. Box 45100, Guadalajara, Jalisco, Mexico
Venue:
Neural Computing and Applications
Year:
2011

Citing 0
Cited 3

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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.