ML-based Risk Assessment in Software Development Lifecycle

  • Authors

    • Arvind Menon Senior Project Manager, Infosys Ltd, India. Author
    • Karthik Raman Software Architect, Tata Consultancy Services, India. Author

    DOI:

    https://doi.org/10.67228/3142788X/IJMLPA-2020PII6D3W

    Published 10-03-2020

  • Software Development Lifecycle (SDLC), Risk Assessment, Machine Learning, Predictive Analytics, Software Project Management, Risk Mitigation, Software Metrics

    Issue

    Vol. 3 No. 2 (2020)

    Section

    Articles

    How to Cite

    [1]
    A. Menon and K. Raman, “ML-based Risk Assessment in Software Development Lifecycle”, IJMLPA, vol. 3, no. 2, pp. 01–05, Oct. 2020, doi: 10.67228/3142788X/IJMLPA-2020PII6D3W.

  • 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.

  • References

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