Article
Enhancing Software Maintenance Through Ensemble Learning-Based Bug Report Prediction
In software development, analyzing large volumes of bug reports is a challenging and time-consuming task. This paper proposes an ensemble machine learning-based model combined with Natural Language Processing (NLP) techniques to automatically predict the nature of bug reports. Multiple classifiers, including Logistic Regression, Naïve Bayes, Support Vector Machine, and Random Forest, are integrated using a voting mechanism to improve prediction accuracy. Experimental results demonstrate that the proposed system significantly enhances classification performance and reduces manual effort in software maintenance.
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