Abstract:
Software defects are a significant concern in the software development industry, leading to increased costs, delays, and compromised quality. To address these challenges, our study focuses on utilizing machine learning algorithms for the early prediction of software defects. By analyzing historical defect data and relevant metrics, we aim to develop a predictive model that can identify potential defects before they occur in the final product. This proactive approach can help developers prioritize testing efforts, allocate resources efficiently, and improve overall software quality. In this research, we employ various machine learning algorithms, with a particular focus on the Random Forest algorithm, recognized for its effectiveness in classification tasks. Our model is trained and tested on multiple datasets, including those from NASA, to evaluate its performance in different scenarios. Key performance metrics such as accuracy, precision, recall, and F-measure are used to assess the model's effectiveness. The findings of our study demonstrate that the Random Forest algorithm achieves superior performance in predicting software defects, with significant improvements in prediction accuracy and reliability. Our model not only identifies defect-prone areas in the code but also provides actionable insights for developers to address potential issues early in the development cycle. The proposed machine learning-based approach to defect prediction offers a robust tool for enhancing software quality assurance processes. By integrating this model into the software development lifecycle, organizations can reduce maintenance costs,
