A comparative study of machine learning algorithm for software defect prediction

Author(s)	Mr. Vijay Anand Yadav, Dr. D L Gupta
Country	India
Abstract	Software defects are among the leading causes of software failure, causing performance degradation, increased costs, and compromised reliability. This research introduces a hybrid machine learning-based SDP model that integrates Random Forest, Support Vector Machines (SVM), Convolutional Neural Networks (CNN), and Logistic Regression to predict software bugs with improved accuracy.
Keywords	Software Defect Prediction (SDP), Machine Learning , Hybrid Model , Random Forest , Support Vector Machine (SVM) , Convolutional Neural Network (CNN) , Logistic Regression , Software Quality Assurance , Bug Prediction , NASA Dataset , Eclipse Dataset , Precision, Recall, F1-Score
Field	Engineering
Published In	Volume 7, Issue 4, July-August 2025
Published On	2025-08-24
DOI	https://doi.org/10.36948/ijfmr.2025.v07i04.53768

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International Journal For Multidisciplinary Research