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

IF 4 3区计算机科学 Q1 COMPUTER SCIENCE, HARDWARE & ARCHITECTURE

Computers & Electrical Engineering Pub Date : 2025-03-21 DOI:10.1016/j.compeleceng.2025.110266

Xiaoquan Chen , Jian Liu , Yingkai Zhang , Qinsong Hu , Yupeng Han , Ruqi Zhang , Jingqi Ran , Lei Yan , Baiqi Huang , Shengtin Ma

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引用次数: 0

Abstract

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.

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来源期刊

Computers & Electrical Engineering 工程技术-工程：电子与电气

CiteScore

9.20

自引率

7.00%

发文量

661

审稿时长

47 days

期刊介绍： The impact of computers has nowhere been more revolutionary than in electrical engineering. The design, analysis, and operation of electrical and electronic systems are now dominated by computers, a transformation that has been motivated by the natural ease of interface between computers and electrical systems, and the promise of spectacular improvements in speed and efficiency. Published since 1973, Computers & Electrical Engineering provides rapid publication of topical research into the integration of computer technology and computational techniques with electrical and electronic systems. The journal publishes papers featuring novel implementations of computers and computational techniques in areas like signal and image processing, high-performance computing, parallel processing, and communications. Special attention will be paid to papers describing innovative architectures, algorithms, and software tools.