In today’s world, where the complexity of software projects is constantly increasing, the development of hybrid intelligent systems for decision support is becoming extremely relevant. This research presents novel models and methods aimed at enhancing the decision-making process in complex software projects. A hybrid intelligent decision support system was developed by integrating agent-based modeling, data-driven analytics, and agile project management principles. It has been shown that the proposed system improves decision accuracy by 18% and reduces project-related risks by 22% compared to conventional project management approaches. New algorithms for decision-making under uncertainty and complexity were developed and tested in simulated environments. The results obtained demonstrate the adaptability and effectiveness of the hybrid approach in dynamic project conditions. It was also established that combining artificial intelligence techniques with traditional methodologies enables faster response to changes in requirements and technology. Hence, the study confirms the feasibility and efficiency of hybrid intelligent systems in supporting managerial decisions throughout the entire software project lifecycle. The findings can be applied to improve project planning, risk mitigation, and overall project quality. This research contributes to the theoretical and practical advancement of decision support systems in the field of software engineering.
Ivanyna et al. (Wed,) studied this question.
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