TraceAwareness and dual-strategy fuzz testing: Enhancing path coverage and crash localization with stochastic science and large language models

This paper proposes an innovative fuzzing technique to address path coverage and crash localization challenges inherent in traditional methods. We introduce TraceAwareness, a technology for precise tracking and recording of program execution paths, significantly enhancing fuzzing efficiency and issue traceability. Additionally, we present a dual-strategy method (DSM-SST-LLMT) based on stochastic science theory and large language model technology, combining random exploration with intelligent analysis for effective test input generation. Experimental evaluations demonstrate that our technique achieves 85% edge coverage compared to AFL++’s 35%, discovers 3,000 new paths versus AFL++’s 800, and identifies 8 critical crashes where AFL++ found none. Our approach shows particular strength in handling complex and diverse inputs, reaching 2-3 times the maximum path depth of AFL++. This research offers new directions for improving software testing efficiency and reliability, with potential applications in critical infrastructure, cloud-based systems, and IoT environments.