INVESTIGATE HOW MACHINE LEARNING ALGORITHMS CAN PREDICT POTENTIAL DEFECTS EARLY IN THE DEVELOPMENT PROCESS BASED ON HISTORICAL TEST DATA

Early defect prediction in software development is crucial for improving efficiency, reducing costs, and maintaining quality in complex systems. This study explores the application of machine learning algorithms to analyze historical test data, including defect logs, code metrics, and testing results, to predict potential defects early in the development process. Commonly used algorithms, such as decision trees, support vector machines, and neural networks, are reviewed, along with the challenges of data preprocessing and model accuracy. The findings highlight the significant benefits of integrating machine learning into defect prediction workflows, enabling proactive issue resolution and streamlined software delivery.