Software reusability metrics estimation: Algorithms, models and optimization techniques

Abstract Objective In this paper, the proposed model is intended to employ a novel evolutionary computing-based artificial intelligence or machine learning scheme for regression tests to be used for reusability estimation. Such enhancement can lead to accurate reusability pattern estimation, which can be effective for optimal software design purposes. This model is popularly called an aging-resilient software reusability forecast representation. The proposed system employs predominant object-oriented software metrics, such as Chidamber and Kemerer's metrics to examine reusability. Here, cumulative metrics, object-oriented metrics, McCabe's metrics, cohesion and a coupling-based reusability assessment model have been proposed which could be of paramount significance in software design optimization. In this paper, software metrics algorithms and their primary constructions have been developed for estimating the metrics from the UML/class diagrams. It is feasible to derive an efficient and robust reusability prediction model for web-service products using object-oriented metrics. Here, it was also found that OO-CK metrics, particularly complexity, cohesion and coupling-related metrics can be helpful in predicting reusability in web-service software products. Considering the above-mentioned key contributions, it can be stated that the proposed research could be of paramount significance in next-generation software computation systems, primarily for software component reusability, reliability, survivability, aging prediction and stability, and for software excellence assurance purposes.