Machine Learning Approaches to Estimating Software Development Effort
IEEE Transactions on Software Engineering
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ISESE '03 Proceedings of the 2003 International Symposium on Empirical Software Engineering
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IEEE Transactions on Software Engineering
A Comparison of Software Project Overruns-Flexible versus Sequential Development Models
IEEE Transactions on Software Engineering
A Systematic Review of Software Development Cost Estimation Studies
IEEE Transactions on Software Engineering
Journal of Systems and Software
An empirical validation of a neural network model for software effort estimation
Expert Systems with Applications: An International Journal
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SNPD '09 Proceedings of the 2009 10th ACIS International Conference on Software Engineering, Artificial Intelligences, Networking and Parallel/Distributed Computing
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Expert Systems with Applications: An International Journal
SERA '10 Proceedings of the 2010 Eighth ACIS International Conference on Software Engineering Research, Management and Applications
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Accurate estimation of software development parameters such as effort, cost, and schedule is very important for effectively managing software development projects. Several software development effort estimation models have been developed in the last few decades. Determining, which is the best estimation model is difficult to decide for a software management team. In this paper we have compared Neural Network models and regression model for software development effort estimation. The comparison reveals that the Neural Network (NN) is better for effort prediction compared to regression analysis model. Further, we have compared two Neural Network models - Feed-Forward Neural Network (FFNN) and Radial Basis Neural Network (RBNN). The evaluation of the models is based on Mean Magnitude Relative Error (MMRE), Relative Standard Deviation (RSD) and Root Mean Squared Error (RMSE). The experimental results show that the RBNN model exhibits better prediction ability than FFNN.