An empirical validation of software cost estimation models
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Software effort estimation by analogy and "regression toward the mean"
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Applied Intelligence
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Applied Intelligence
The adjusted analogy-based software effort estimation based on similarity distances
Journal of Systems and Software
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IEEE Transactions on Fuzzy Systems
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Expert Systems with Applications: An International Journal
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ACAI '11 Proceedings of the International Conference on Advances in Computing and Artificial Intelligence
Systematic literature review of machine learning based software development effort estimation models
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Optimizing the modified fuzzy ant-miner for efficient medical diagnosis
Applied Intelligence
Radial basis function network using intuitionistic fuzzy C means for software cost estimation
International Journal of Computer Applications in Technology
LMES: A localized multi-estimator model to estimate software development effort
Engineering Applications of Artificial Intelligence
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The ability to accurately and consistently estimate software development efforts is required by the project managers in planning and conducting software development activities. Since software effort drivers are vague and uncertain, software effort estimates, especially in the early stages of the development life cycle, are prone to a certain degree of estimation errors. A software effort estimation model which adopts a fuzzy inference method provides a solution to fit the uncertain and vague properties of software effort drivers. The present paper proposes a fuzzy neural network (FNN) approach for embedding artificial neural network into fuzzy inference processes in order to derive the software effort estimates. Artificial neural network is utilized to determine the significant fuzzy rules in fuzzy inference processes. We demonstrated our approach by using the 63 historical project data in the well-known COCOMO model. Empirical results showed that applying FNN for software effort estimates resulted in slightly smaller mean magnitude of relative error (MMRE) and probability of a project having a relative error of less than or equal to 0.25 (Pred(0.25)) as compared with the results obtained by just using artificial neural network and the original model. The proposed model can also provide objective fuzzy effort estimation rule sets by adopting the learning mechanism of the artificial neural network.