An Adaptive Mutation-Aware Test Case Ordering Framework Using Deep Learning and Quantum-Behaved Multi-Objective PSO

 

 

 

S. Sowmyadevi1,*, Anna Alphy1

 

1Department of CSE, SRMIST, Delhi-NCR campus, Ghaziabad, 201204, Uttar Pradesh, India

 

Text Box: Abstract 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. Emails: ss2860@srmist.edu.in; annaa@srmist.edu.in

 

Received: March 16, 2025 Revised: June 09, 2025 Accepted: July 14, 2025

 

Keywords: Secure honeynet, Cloud IoT model; Machine learning algorithms; Health monitoring; Particle colony optimization