Fuzzy Logic Model for Informed Decision-Making in Risk Assessment During Software Design

Software development projects are highly susceptible to risks during the design phase, which plays a crucial role in shaping the architecture, functionality, and quality of the final product. Decisions made during the design stage significantly affect the outcomes of the subsequent phases, including coding, testing, deployment, and maintenance. However, the complexities and uncertainties inherent in the design phase are often inadequately addressed by traditional risk management tools as they rely on deterministic models that oversimplify interdependent risks. This research introduces a fuzzy logic-based risk assessment model tailored specifically for the design phase of software development projects. The proposed fuzzy model, unlike the existing state-of-the-art models, regards the iterative nature of the design phase, the interaction between diverse stakeholders, and the potential inconsistencies that may arise between the initial and final version of the software design. More specifically, it develops a customized fuzzy model that incorporates design-specific risk factors such as evolving architectural requirements, technical feasibility concerns, and stakeholder misalignment. Finally, it integrates expert-driven rule definitions to enhance model accuracy and real-world applicability, ensuring that risk assessments reflect actual challenges faced by software design teams. Simulations conducted across diverse real-world scenarios demonstrate the model's robustness in predicting risk levels and supporting mitigation strategies. The simulation results confirm that the proposed fuzzy logic model outperforms conventional approaches by offering greater flexibility and adaptability in managing design-phase risks, assisting project managers in prioritizing mitigation efforts more effectively to improve project outcomes.