| Abstract | Companies usually have limited amount of data for effort estimation. Machine learning methods have been preferred over parametric models due to their flexibility to calibrate the model for the available data. On the other hand, as machine learning methods become more complex, they need more data to learn from. Therefore the challenge is to increase the performance of the algorithm when there is limited data. In this paper, we use a relatively complex machine learning algorithm, neural networks, and show that stable and accurate estimations are achievable with an ensemble using associative memory. Our experimental results show that our proposed algorithm (ENNA) produces significantly better results than neural network (NN) in terms of accuracy and robustness. We also analyze the effect of feature subset selection on ENNA’s estimation performance in a wrapper framework. We show that the proposed ENNA algorithm that use the features selected by the wrapper does not perform worse than those that use all available features. Therefore, measuring only company specific key factors is sufficient to obtain accurate and robust estimates about software cost estimation using ENNA. |
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