Abstract

 Software effort estimation research shows that there is no universal agreement on the “best” effort estimation approach. This is largely due to the “ranking instability” problem, which is highly contingent on the evaluation criteria and the subset of the data used in the investigation. There are a large number of different method combination exists for software effort estimation, selecting the most suitable combination becomes the subject of research in this paper. Unless we can reasonably determine stable rankings of different estimators, we cannot determine the most suitable estimator for effort estimation. This paper reports an empirical study using 90 estimation methods applied to 20 datasets as an attempt to address this question. One of the commonly used machine learning techniques is the analogy method that cannot handle the categorical variables efficiently. In general, project attributes of cost estimation are often measured in terms of linguistic values. These imprecise values leads to analogous while explaining the process. The proposed fuzzy analogy method is a new approach based on reasoning by analogy using fuzzy logic for handling both numerical and categorical variables where the uncertainty and imprecision solution is also identified by the behavior of linguistic values utilized in the software projects. The performance of this method validates the results based on historical NASA dataset. The outcome of fuzzy analogy method is analyzed which indicates its improvement over the existing fuzzy logic methods. Estimation by analogy can be significantly improved by a dynamic selection of nearest neighbors, using only the project data from regions with small variance.

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