ML-based Risk Assessment in Software Development Lifecycle

Abstract

Risk assessment is a critical component of the Software Development Lifecycle (SDLC) to ensure timely delivery, maintain quality, and reduce project failures. Traditional risk assessment approaches rely heavily on expert judgment and manual analysis, which can be subjective and prone to errors. This paper proposes a Machine Learning (ML)-based risk assessment framework for SDLC, leveraging historical project data and software metrics to predict potential risks at different stages of development. Several ML algorithms, including Random Forest, Support Vector Machine, and Neural Networks, are evaluated for their effectiveness in identifying high-risk components. Experimental results demonstrate that the proposed approach can enhance risk prediction accuracy, support proactive mitigation strategies, and improve overall software project success rates. The framework provides a scalable and data-driven solution for early risk detection in modern software engineering practices.