An Empirical Analysis for Predicting Source Code File Reusability Using Meta-Classification Algorithms

Although various quantifiers of software component reusability have been proposed, these metrics have seldom been utilized in existing literature to analyze source code file reusability transpiring within a single product family. Such metrics can be effortlessly employed to develop reuse prediction models which can support the software practitioners in obtaining information regarding the total cost involved in developing a novel version of a prevailing software or upgrading an existing software version by estimating the total reusable code files in advance without being compelled to scrutinize the complete codebase. In view of this, this research work aims to examine the efficacy of seven meta-classification techniques in the development of such reuse prediction models on four software datasets constructed from four successively released versions of software using appropriate reuse metrics. We also evaluate the predictive performance of these meta-classifiers against the statistical technique of logistic regression and rank these techniques using the Friedman statistical test.