Machine Learning Model to Enhance the Quality of Software Development Risk Management

This research introduces a novel approach to software development risk management using machine learning. This approach is based on the analysis of historical data from previous software projects to predict and mitigate the risks in future software development efforts. This machine learning model considers various factors such as the size of development team, project complexity, software budget, and the other required factors to provide more accurate risk assessments and support the decision-making process to achieve an acceptable level of software quality development. The implementation and benefits of using a machine learning model for software development risk management are discussed, including improved efficiency, reduced risk, and increased predictability. The model is trained and showed good results. This article also highlights the potential challenges and limitations of the proposed approach and suggests areas for future research and improvement.