Clustering and Local Regression in Object Oriented Metrics

This paper gives a brief review of clustering and local regression techniques; we are mainly focused on its implementation to software engineering data and we present an example of the preliminary results using clustering and local regression. The clustering, and local regression are part of the data processing of a project called Analysis of Software Engineering Data Using Computational Intelligence Techniques. The project takes advantage of Genetic Algorithms (GA), Genetic Programming (GP), and Local Regression to find models to describe the data we are dealing with. The clustering technique is used to find similar structures in the dataset before it is introduced to the GA, GP, and Local Regression algorithms. The final goal of this work is to build mathematical models to determine the number of fixes in software projects. Number of fixes is our dependent variable while CK-Metrics and lines of code (LOC) are the independent variables of the system.