A genetic algorithm to configure support vector machines for predicting fault-prone components
PROFES'11 Proceedings of the 12th international conference on Product-focused software process improvement
How multi-objective genetic programming is effective for software development effort estimation?
SSBSE'11 Proceedings of the Third international conference on Search based software engineering
Systematic literature review of machine learning based software development effort estimation models
Information and Software Technology
Search-based approaches for software development effort estimation
Proceedings of the 12th International Conference on Product Focused Software Development and Process Improvement
Systematic adoption of genetic programming for deriving software performance curves
ICPE '12 Proceedings of the 3rd ACM/SPEC International Conference on Performance Engineering
Single and Multi Objective Genetic Programming for software development effort estimation
Proceedings of the 27th Annual ACM Symposium on Applied Computing
A grammatical evolution approach for software effort estimation
Proceedings of the 15th annual conference on Genetic and evolutionary computation
A PSO-based model to increase the accuracy of software development effort estimation
Software Quality Control
LMES: A localized multi-estimator model to estimate software development effort
Engineering Applications of Artificial Intelligence
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Context: The use of search-based methods has been recently proposed for software development effort estimation and some case studies have been carried out to assess the effectiveness of Genetic Programming (GP). The results reported in the literature showed that GP can provide an estimation accuracy comparable or slightly better than some widely used techniques and encouraged further research to investigate whether varying the fitness function the estimation accuracy can be improved. Aim: Starting from these considerations, in this paper we report on a case study aiming to analyse the role played by some fitness functions for the accuracy of the estimates. Method: We performed a case study based on a publicly available dataset, i.e., Desharnais, by applying a 3-fold cross validation and employing summary measures and statistical tests for the analysis of the results. Moreover, we compared the accuracy of the obtained estimates with those achieved using some widely used estimation methods, namely Case-Based Reasoning (CBR) and Manual Step Wise Regression (MSWR). Results: The obtained results highlight that the fitness function choice significantly affected the estimation accuracy. The results also revealed that GP provided significantly better estimates than CBR and comparable with those of MSWR for the considered dataset.