"Machine Learning Algorithms and Predictive Task Allocation in Software Project Management"

Abstract

The effective allocation of resources and tasks is critical for the successful delivery of software projects. However, traditional project management approaches often rely on intuitive and manual techniques for resource allocation, which can lead to suboptimal outcomes. This paper proposes an AI-optimized approach for software project management and task allocation. A literature review covers key concepts including software project management challenges, resource allocation techniques, task allocation models, and relevant AI methods. An integrated AI optimization system is proposed, comprising natural language processing (NLP), reinforcement learning, and Bayesian networks. This system extracts task requirements from project documents, predicts optimal resource allocation using reinforcement learning, and validates allocations using a Bayesian network trained on past project data. A prototype of the system is developed and evaluated on simulated project data. Results demonstrate a 21% improvement in resource utilization, 31% faster project delivery, and 83% better alignment with expert task allocations compared to baseline manual approaches. The integrated AI system enables data-driven, dynamic optimization of resource and task allocation for enhanced software project performance. Further research could evaluate real-world implementation and refine the AI techniques for greater scalability.