Journal of Intelligent Systems and Internet of Things JISIoT 2690-6791 2769-786X 10.54216/JISIoT https://www.americaspg.com/journals/show/3944 2019 2019 Department of CSE, SRMIST, Delhi-NCR campus, Ghaziabad, 201204, Uttar Pradesh, India S. S. Department of CSE, SRMIST, Delhi-NCR campus, Ghaziabad, 201204, Uttar Pradesh, India Anna Alphy   In regression testing, rapidly identifying defects is crucial for maintaining software quality amid frequent code changes. Traditional test case ordering methods, despite extensive research, often overlook the subtle but important relationship between test executions and mutations introduced during code modifications. This paper presents an adaptive mutation-aware test case ordering framework that integrates predictive modeling with swarm-based multi-objective optimization to address this gap. The approach begins by transforming test cases into enriched feature vectors, incorporating mutation coverage, historical performance, execution cost, and statement-level weighting. A supervised deep learning model is employed to predict the likelihood of each test case uncovering seeded defects. These predictions are subsequently fed into a Quantum-Behaved Particle Swarm Optimization (QPSO) engine, which generates an optimal execution sequence by jointly optimizing fault detection, execution cost, reuse potential, and coverage diversity. The proposed framework is demonstrated using a simple Java program and rigorously validated on real-world projects from the De-fects4J benchmark. Experimental results consistently show improvements in APFD, mutation scores, and execution efficiency, confirming the feasibility and scalability of the proposed system. 2026 2026 194 206 10.54216/JISIoT.180114 https://www.americaspg.com/articleinfo/18/show/3944