Reusability hypothesis verification using machine learning techniques: a case study

Since the emergence of object technology, organizations have accumulated a tremendous amount of object-oriented (OO) code. Instead of continuing to recreate components that are 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. This paper proposes an experiment to verify three hypotheses about the impact of three internal characteristics (inheritance, coupling and complexity) of OO applications on reusability. This verification is done through a machine learning approach (the C4.5 algorithm and a windowing technique). Two kinds of results are produced: (1) for each hypothesis (characteristic), a predictive model is built using a set of metrics derived from this characteristic; and (2) for each predictive model, we measure its completeness, correctness and global accuracy.