Using Deep Learning for Deadlock Detection in Intelligent Software Systems

This paper addresses deadlocks in concurrent software systems, presenting an AI-driven testing framework for detecting deadlocks in task interactions. Using advanced machine learning techniques, the system monitors task interactions, learns from historical data, and refines its detection ability. The approach formalizes task properties, such as states, resource demands, and dependencies, to build a robust interaction model. A supervised neural network algorithm provides real-time predictions, with evaluation metrics like detection accuracy and false positive rates for comparison with conventional methods. Results show significant improvements in deadlock detection accuracy, addressing scalability and manageability challenges faced by traditional solutions, offering a scalable AI alternative for modern distributed environments.