Vachik S. Dave
Abstract
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.
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Index Terms
- Comparison of regression model, feed-forward neural network and radial basis neural network for software development effort estimation
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