aspg-18-3944-1792025449 20261015005049 American Scientific Publishing Group admin@americaspg.com American Scientific Publishing Group Journal of Intelligent Systems and Internet of Things JISIoT 2769-786X 2690-6791 2026 18 1 S. Sowmyadevi Department of CSE, SRMIST, Delhi-NCR campus, Ghaziabad, 201204, Uttar Pradesh, India Anna Alphy Department of CSE, SRMIST, Delhi-NCR campus, Ghaziabad, 201204, Uttar Pradesh, India Normal false false false EN-US X-NONE AR-SA DefSemiHidden="false" DefQFormat="false" DefPriority="99" LatentStyleCount="371"> UnhideWhenUsed="true" QFormat="true" Name="heading 2"/> UnhideWhenUsed="true" QFormat="true" Name="heading 3"/> UnhideWhenUsed="true" QFormat="true" Name="heading 4"/> UnhideWhenUsed="true" QFormat="true" Name="heading 5"/> UnhideWhenUsed="true" QFormat="true" Name="heading 6"/> UnhideWhenUsed="true" QFormat="true" Name="heading 7"/> UnhideWhenUsed="true" QFormat="true" Name="heading 8"/> UnhideWhenUsed="true" QFormat="true" Name="heading 9"/> Name="index 1"/> Name="index 2"/> Name="index 3"/> Name="index 4"/> Name="index 5"/> Name="index 6"/> Name="index 7"/> Name="index 8"/> Name="index 9"/> UnhideWhenUsed="true" Name="toc 1"/> UnhideWhenUsed="true" Name="toc 2"/> 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Colorful Accent 5"/> Name="Grid Table 1 Light Accent 6"/> Name="Grid Table 6 Colorful Accent 6"/> Name="Grid Table 7 Colorful Accent 6"/> Name="List Table 1 Light Accent 1"/> Name="List Table 6 Colorful Accent 1"/> Name="List Table 7 Colorful Accent 1"/> Name="List Table 1 Light Accent 2"/> Name="List Table 6 Colorful Accent 2"/> Name="List Table 7 Colorful Accent 2"/> Name="List Table 1 Light Accent 3"/> Name="List Table 6 Colorful Accent 3"/> Name="List Table 7 Colorful Accent 3"/> Name="List Table 1 Light Accent 4"/> Name="List Table 6 Colorful Accent 4"/> Name="List Table 7 Colorful Accent 4"/> Name="List Table 1 Light Accent 5"/> Name="List Table 6 Colorful Accent 5"/> Name="List Table 7 Colorful Accent 5"/> Name="List Table 1 Light Accent 6"/> Name="List Table 6 Colorful Accent 6"/> Name="List Table 7 Colorful Accent 6"/> /* Style Definitions */ table.MsoNormalTable {mso-style-name:"Table Normal"; mso-tstyle-rowband-size:0; mso-tstyle-colband-size:0; mso-style-noshow:yes; mso-style-priority:99; mso-style-parent:""; mso-padding-alt:0in 5.4pt 0in 5.4pt; mso-para-margin-top:0in; mso-para-margin-right:0in; mso-para-margin-bottom:10.0pt; mso-para-margin-left:0in; line-height:115%; mso-pagination:widow-orphan; font-size:11.0pt; font-family:"Calibri",sans-serif;} 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 194 206 3944 https://creativecommons.org/licenses/by/4.0/ 10.54216/JISIoT.180114 https://americaspg.com/journal/18/article/3944 https://americaspg.com/storage/81753399971.pdf