Models and Methodologies for Optimizing Software Project Performance: An Integrative Review of Artificial Intelligence-Assisted Planning, Resource Allocation, Human Factors, and Data-Driven Decision Support

Author

Bogdan Rânea, Alexandru Marin


Abstract

 Software project performance is influenced by planning quality, resource allocation, human factors, risk, monitoring, and the growing use of artificial intelligence. This paper presents an integrative review of models and methodologies that can support the optimization of software project performance. Literature from project management, software engineering, artificial intelligence, optimization, and human-centered systems was analyzed, with emphasis on studies published between 2020 and September 2026. The review groups existing approaches into planning and scheduling, human-resource allocation, predictive and risk analysis, monitoring and early detection, and adaptive decision support. The synthesis shows that artificial intelligence and optimization methods can improve estimation, task allocation, monitoring, and managerial decision support, but their effectiveness depends on data quality, transparency, organizational context, and human oversight. Evidence from software-intensive engineering applications also shows the value of transferable methods for projects with interacting technical and human constraints. The review concludes that software project performance is best addressed through a continuous feedback process connecting project inputs, intelligent analysis, managerial decisions, and execution data. This integrated perspective can support more adaptive and data-informed software project management.



Keywords

Software project management; Artificial intelligence; Project performance; Resource allocation; Human factors; Decision support



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References


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