Hybrid morphological methodology for software development cost estimation
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摘要
In this paper we propose a hybrid methodology to design morphological-rank-linear (MRL) perceptrons in the problem of software development cost estimation (SDCE). In this methodology, we use a modified genetic algorithm (MGA) to optimize the parameters of the MRL perceptron, as well as to select an optimal input feature subset of the used databases, aiming at a higher accuracy level for SDCE problems. Besides, for each individual of MGA, a gradient steepest descent method is used to further improve the MRL perceptron parameters supplied by MGA. Finally, we conduct an experimental analysis with the proposed methodology using six well-known benchmark databases of software projects, where two relevant performance metrics and a fitness function are used to assess the performance of the proposed methodology, which is compared to classical machine learning models presented in the literature.
论文关键词:Software development cost estimation,Morphological-rank-linear perceptrons,Genetic algorithms,Hybrid methodologies,Feature selection
论文评审过程:Available online 16 December 2011.
论文官网地址:https://doi.org/10.1016/j.eswa.2011.11.077