Optimization Accuracy of Software Defect Prediction using Random Forest Machine Learning Algorithm
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Abstract
Software defect prediction is a critical task in software engineering that aims to
identify faulty components early in the development lifecycle, thereby reducing cost and
improving software quality. This project explores the effectiveness of the Random Forest
machine learning algorithm for predicting software defects, with a particular focus on
optimizing model accuracy. The Random Forest algorithm, known for its robustness and ability
to handle high-dimensional data, is applied to benchmark software defect datasets. The study
involves extensive preprocessing, including feature selection and normalization, followed by
hyperparameter tuning to enhance prediction performance. Evaluation metrics such as
accuracy, precision, recall, F1-score, and AUC-ROC are used to assess the model's
effectiveness. Experimental results demonstrate that the optimized Random Forest model
achieves high predictive accuracy and outperforms several baseline models. This work
highlights the potential of ensemble learning methods, particularly Random Forest, as a reliable
approach for software defect prediction, aiding developers in creating more reliable and
maintainable software systems.
Article Details
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
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