Reconnoitering Static Analysis Metrics for Predicting Software Component Reusability Using Ensemble Model
Keywords:
Ensemble Model, Machine Learning, Prediction, Reusability, SoftwareAbstract
Data mining and machine learning have created new avenues for creating software and investigation. The software development life cycle (SDLC) has also benefited from incorporating machine learning, opening up prospects for efficient and well-planned growth. The SDLC includes the reusability of software as a key component. Software reuse management thus assumes an active part in the SDLC. It reduces the expense and time needed to produce a software application. Evaluating a software component's reusability, or how appropriate it is for reuse, is a key difficulty in this scenario. The most effective methods for determining whether a particular software part is reusable or not come from machine learning to evaluate reusability; this study aims to create an ensemble machine learning model that integrates Support Vector Machine, K-Nearest Neighbour, Decision Tree, Artificial Neural Network, and Naive Bayes. After pre-processing, the publicly accessible benchmark dataset is used for experimentation. Compared with base classifiers, the suggested model delivered the most favourable results, with accuracy, precision, recall, and f1-score values of 89.48%, 0.9406, 0.9484, and 0.9445, respectively. According to the assessment of our technique, our approach can accurately evaluate reusability as experienced by engineers.
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