Software Project Similarity Measurement Based on Fuzzy C-Means

A reliable and accurate similarity measurement between two software projects has always been a challenge for analogy-based software cost estimation. Since the effort for a new project is retrieved from similar historical projects, it is essentially to use the appropriate similarity measure that finds those close projects which in turn increases the estimation accuracy. In software engineering literature, there is a relatively little research addressed the issue of how to find out similarity between two software projects when they are described by numerical and categorical features. Despite simplicity of exiting similarity techniques such as: Euclidean distance, weighted Euclidean distance and maximum distance, it is hard to deal with categorical features. In this paper we present two approaches to measure similarity between two software projects based on fuzzy C-means clustering and fuzzy logic. The new approaches are suitable for both numerical and categorical features.