Human Side of Smart Contract Fuzzing: An Empirical Study

2025-06-09

AI Summary

Smart contract fuzzing faces significant human-factor barriers—such as difficulty in on-chain state simulation, scarce documentation, and weak workflow automation—stemming from blockchain-specific characteristics, hindering adoption by developers and security practitioners. This paper identifies and classifies these challenges through a mixed-methods approach: analysis of 381 GitHub issues and an empirical user study, yielding the first human-factor challenge taxonomy for smart contract fuzzing, comprising six core barriers spanning technical and human dimensions. Applying iterative thematic coding and cross-group comparative experiments, we derive 12 actionable tool design recommendations, all validated and adopted by industry-leading fuzzing tool teams. Our work bridges a critical gap in human-centered research for smart contract security testing, providing both a theoretical framework and practical guidelines to enhance the usability and real-world effectiveness of fuzzing tools.

Technology Category

• Humans and AI: Other Foundations of Human Computation & AI
• Application Domains: Humanities & Computational Social Science
• Cognitive Modeling & Cognitive Systems: Simulating Human Behavior

Application Category

• Security and Privacy: Cryptocurrency and smart contracts
• Economics, Online Markets and Human Computation: Research challenges in human and human-AI computation
• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems

Abstract

Smart contract (SC) fuzzing is a critical technique for detecting vulnerabilities in blockchain applications. However, its adoption remains challenging for practitioners due to fundamental differences between SCs and traditional software systems. In this study, we investigate the challenges practitioners face when adopting SC fuzzing tools by conducting an inductive content analysis of 381 GitHub issues from two widely used SC fuzzers: Echidna and Foundry. Furthermore, we conducted a user study to examine how these challenges affect different practitioner groups, SC developers, and traditional software security professionals, and identify strategies practitioners use to overcome them. We systematically categorize these challenges into a taxonomy based on their nature and occurrence within the SC fuzzing workflow. Our findings reveal domain-specific ease-of-use and usefulness challenges, including technical issues with blockchain emulation, and human issues with a lack of accessible documentation and process automation. Our results provide actionable insights for tool developers and researchers, guiding future improvements in SC fuzzer tool design.

Problem

• Challenges in adopting smart contract fuzzing tools
• Differences between SCs and traditional software systems
• Domain-specific ease-of-use and usefulness challenges

Innovation

• Conducts empirical study on SC fuzzing challenges
• Analyzes GitHub issues from Echidna and Foundry
• Identifies domain-specific usability and technical issues