An empirically based object-oriented testing using Machine learning
DOI:
https://doi.org/10.4108/eetiot.5344Keywords:
Software defect, Machine learning, Early defect prediction, Software Quality assurance, stacking classifierAbstract
INTRODUCTION: The rapid growth of machine learning has the potential to revolutionize various industries and applications by automating complex tasks and enhancing efficiency. Effective software testing is crucial for ensuring software quality and minimizing resource expenses in software engineering. Machine learning techniques play a vital role in software testing by aiding in test case prioritization, predicting software defects, and analyzing test results.
OBJECTIVES: The primary objective of this study is to explore the use of machine learning algorithms for software defect prediction.
METHODS: Machine Learning models including Random Forest Classifier, Logistic Regression, K Nearest Neighbors, Gradient Boosting Classifiers, Catboost Classifier, and Convolutional Neural Networks have been employed for the study. The dataset includes a wide range of features relevant to software defect prediction and evaluates the performance of different prediction models. The study also focussed on developing hybrid models using stacking classifiers, which combine multiple individual models to improve accuracy.
RESULTS: The experimental results show that the hybrid models combining CatBoost and Convolutional Neural Network have outperformed individual models, achieving the highest accuracy of 89.5%, highlighting the effectiveness of combining machine learning algorithms for software defect prediction.
CONCLUSION: In conclusion, this study sheds light on the pivotal role of machine learning in enhancing software defect prediction.
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