Abstract
Estimation by Analogy is a popular method in the field of software cost estimation. However, the configuration of the method affects estimation accuracy, which has a great effect on project management decisions. This paper proposes an optimal global setup for determining empirically the best parameter configuration based on genetic algorithms. Those parameters involve the definition of project similarity, the number of analogies and the way of adjusting the analogies used. We describe how such a search can be performed in the parameter space spanned by these parameters, which are essentially of different type. We report results on two datasets and compare with approaches that explore partially the search space. Results provide evidence that our method produces similar or better accuracy figures with respect to other approaches.
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