A metrics based detection of reusable object-oriented software components using machine learning algorithm /

Since the emergence of the object technology, organizations have accumulated a tremendous amount of object-oriented (OO) code. Instead of continuing to recreate components similar to existing artifacts, and considering the rising costs of development, many organizations would like to decrease software development costs and cycle time by reusing existing OO components. The difficulty of finding reusable components is that reuse is a complex and thus less quantifiable measure. In this research, we first proposed three reuse hypotheses about the impact of three internal characteristics (inheritance, coupling, and complexity) of OO software artifacts on reusability. Corresponding metrics suites were then selected and extracted. We used C4.5, a machine learning algorithm, to build predictive models from the learning data set that we have obtained from a medium sized software system developed in C++. Each predictive models was then verified according to its completeness, correctness and global accuracy. The verification results proved that the proposed hypotheses were correct. The uniqueness of this research work is that we have combined the state of the art of three different subjects (reuse detection and prediction, OO metrics and their extraction, and applied machine learning algorithm) to form a process of finding interesting properties of OO software components that affect reusability