Abstract: Software reusability is recognized as a crucial aspect of quality. Addressing the software turmoil, increasing software quality, and enhancing performance are the most evident benefits of software reuse. Finding reusable software elements in an identified present structure is a critical yet underdeveloped challenge. Authors employ a method built on software models and metrics to discover and assess reusable software. This investigation aims to evaluate the efficacy and competency of machine learning methods that are being used to create an accurate and useful assessment framework that can evaluate the reusability of software elements using static metrics. In the present work, the authors conduct a thorough literature review of machine learning methods used to forecast software reusability. Initially, background information and relevant studies are presented. After that, a summary of machine learning techniques is provided. Additional research is being done to assess how well different machine learning methods forecast software reusability. The highest-scoring ML classification framework achieved an accuracy of 89.33% (ANN), outperforming other studies in predicting accuracy (e.g., KNN, DT, RF, SVM, BT, KNN, and HMM). The outcomes may be employed to determine which machine learning model is most useful for identifying reusable parts of software since it is accurate, quick, productive, and cost-effective.

Key words: machine learning, prediction, reusability, software, static analysis