Risk Assessment and Mitigation Modeling in Software Development Life Cycle Projects Using Probabilistic Frameworks
Keywords:
SDLC, Risk Assessment, Probabilistic Framework, Monte Carlo Simulation, Bayesian Networks, Software Project Management, Risk Mitigation, Project Failure PredictionSynopsis
Software Development Life Cycle (SDLC) projects are increasingly complex and susceptible to various risks such as budget overruns, technical failures, and delayed schedules. Traditional risk management approaches often lack dynamic adaptability and predictive capabilities. This study presents a probabilistic risk assessment and mitigation framework tailored for SDLC environments. Utilizing methods such as Monte Carlo simulations, Bayesian networks, and decision trees, the model quantifies risk likelihood, impact, and residual exposure post-mitigation. A layered mitigation model is proposed to enhance decision-making during the project lifecycle. Empirical data and simulations demonstrate significant improvements in risk detection accuracy and reduction in project failure rates. The research advocates for the integration of probabilistic models in modern project governance tools.
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