2 Years Impact Factor:
2.85
5 Years Impact Factor: 1.53
Author: G. Keerthana, R Sri Harsha, SajidPasha, A Sravani
Abstract:
The rapid evolution of software development has made it increasingly important to predict software quality effectively. Predicting software quality at different stages of development can significantly enhance the software development lifecycle, minimize errors, and optimize resources. This study presents a comparative analysis of various machine learning algorithms for software quality prediction. We examine several algorithms, including decision trees, support vector machines (SVM), random forests, neural networks, and k-nearest neighbors (KNN), evaluating their performance on different datasets derived from real-world software systems. The objective is to assess the accuracy, efficiency, and effectiveness of these algorithms in predicting software quality metrics, such as defect density, maintainability, reliability, and performance. The findings of this study can assist software engineers in selecting the most suitable machine learning model based on the characteris
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