The swift advancement of the software industry, distinguished by worldwide distribution an‎d extensive connectivity, has exposed the limitations of conventional, intuition-driven project management approaches. Contemporary project managers often contend with "data overload" an‎d cognitive biases, which undermine their ability to make sound decisions concerning project scope, timelines, an‎d resource allocation. This seminar explores the capacity of Decision-Making Support Systems (DMSS) to revolutionize software governance, transitioning it from a reactive practice to a data-informed discipline. Through the incorporation of Machine Learning (ML), Natural Language Processing (NLP), an‎d Computational Intelligence, DMSS architectures are demonstrated to evolve from passive data storage solutions into collaborative systems that offer prescriptive recommendations. Key analytical domains encompass the utilization of predictive analytics for the proactive identification of risks, the application of Natural Language Processing (NLP) to discern ambiguity within project requirements, an‎d the implementation of Reinforcement Learning for the dynamic, real-time allocation of resources. This investigation underscores the capacity of these technologies to navigate the constraints inherent in the "Iron Triangle," concurrently diminishing the risks endemic to Global Software Development. Moreover, the seminar explores pivotal implementation hurdles, including data integrity an‎d organizational resistance, an‎d recommends the integration of Explainable AI (XAI) to foster transparency an‎d confidence in automated recommendations. Consequently, the research ultimately posits that DMSS provides a strategic competitive advantage, thereby substantially improving project success rates an‎d operational resilience. The swift advancement of the software industry, distinguished by worldwide distribution an‎d extensive connectivity, has exposed the limitations of conventional, intuition-driven project management approaches. Contemporary project managers often contend with "data overload" an‎d cognitive biases, which undermine their ability to make sound decisions concerning project scope, timelines, an‎d resource allocation. This seminar explores the capacity of Decision-Making Support Systems (DMSS) to revolutionize software governance, transitioning it from a reactive practice to a data-informed discipline. Through the incorporation of Machine Learning (ML), Natural Language Processing (NLP), an‎d Computational Intelligence, DMSS architectures are demonstrated to evolve from passive data storage solutions into collaborative systems that offer prescriptive recommendations. Key analytical domains encompass the utilization of predictive analytics for the proactive identification of risks, the application of Natural Language Processing (NLP) to discern ambiguity within project requirements, an‎d the implementation of Reinforcement Learning for the dynamic, real-time allocation of resources. This investigation underscores the capacity of these technologies to navigate the constraints inherent in the "Iron Triangle," concurrently diminishing the risks endemic to Global Software Development. Moreover, the seminar explores pivotal implementation hurdles, including data integrity an‎d organizational resistance, an‎d recommends the integration of Explainable AI (XAI) to foster transparency an‎d confidence in automated recommendations. Consequently, the research ultimately posits that DMSS provides a strategic competitive advantage, thereby substantially improving project success rates an‎d operational resilience.

2/18/2026 12:00:00 AM

89741

1404/11/30

Decision-Making Support System for Software Project Managers

سيستم‌هاي پشتيباني تصميم‌گيري (DMSS)، , مديريت پروژه‌هاي نرم‌افزاري، , هوش مصنوعي، , تحليل‌هاي پيش‌بيني‌كننده، , هوش مصنوعي قابل توضيح (XAI)

Decision-Making Support Systems (DMSS),Decision-Making Support Systems (DMSS), , Software Project Management, , Artificial Intelligence, Artificial Intelligence, , Predictive AnalyPredictive Analytics,tics, , Explainable AI (XAI)