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Estimating the amount of effort required for developing a software system is one of the most important project management concerns. This study successfully produces an optimal reduced linear model for software cost estimation by employing a series of methods of multiple regressions to identify the most significant explanatory variables of the fifteen COCOMO cost drivers. The results yielded by the...
Support vector machine (SVM) is a new method based on statistical learning theory. It has been successfully used to solve nonlinear regression and time series problems. However, SVM has rarely been applied to software reliability prediction. In this study, an SVM-based model for software reliability forecasting is proposed. In addition, the parameters of SVM are determined by Genetic Algorithm (GA)...
This paper presents computational intelligence techniques for software cost estimation. We proposed a new recurrent architecture for genetic programming (GP) in the process. Three linear ensembles based on (i) arithmetic mean (ii) geometric mean and (iii) harmonic mean are implemented. We also performed GP based feature selection. The efficacy of these techniques viz multiple linear regression, polynomial...
An evolutionary neural network modeling approach for software cumulative failure prediction based on feed-forward neural network is proposed. A real coded genetic algorithm is used to optimize the mean square of the error produced by training a neural network established by Aljahdali S.. In this paper we present a real coded genetic algorithm that uses the appropriate operators for this encoding type...
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