A study of comparative analysis of regression algorithms for reusability evaluation of object oriented based software components

Reusability of software is found to be a key feature of quality. The most obvious outcomes of software reuse are overcoming the software crisis, advancing in software quality and improving productivity. The issue of spotting reusable software components from given existing system is very important but yet it is not much cultivated. For identification and evaluation of reusable software we use an approach that has foundation of software models and metrics. Idea of this study is to examine the competence and effectiveness of machine learning regression techniques which are experimented here to build precise and constructive evaluation model that can assess the reusability of Object Oriented based software components based on the values of five metrics of metrics suite presented by “Shyam R. Chaidmber and Chris F. Kemerer”. By setting different values of parameters of these algorithms, it is also concluded that which specific algorithm or class of algorithms is appropriate for reusability evaluation and with which parameter's values. For this comparative analysis we have used Weka and experimented different regression techniques as Multi-linear regression, Model Tree M5P, Standard instance-based learning scheme IBk and Meta-learning scheme Additive Regression. As the result of this analysis and experimentation “Standard instance-based learning IBk with no distance weighting” is found to be the best regression algorithm for reusability evaluation of Object Oriented software components using CK metrics.