Improving Coverage and Vulnerability Detection in Smart Contract Testing Using Self-Adaptive Learning GA

Abstract

In the domain of software testing, the generation of test cases is a critical process for detecting system errors and bugs. However, automated test case generation for smart contracts often encounters challenges related to automation, vulnerability diversity, and coverage. This paper presents a novel method, the self-adaptive learning Genetic Algorithm (self-adaptive learning GA), designed to address these issues. Our research methodology incorporates several construction models, namely the Control Dependence Graph (CDG), Control Flow Graph (CFG), and Application Binary Interface (ABI). Initially, the ABI model provides essential information for generating and executing test cases. The CFG model subsequently visualizes potential execution paths through the functions of smart contracts. Ultimately, the CDG model identifies potential vulnerabilities in smart contracts. Using these models, our method enhances automatic test case generation in smart contracts by improving coverage and reducing execution time. We selected a variety of smart contracts from the Decentralized Finance (DeFi) ecosystem for data collection and comparative analysis. The experimental results show superior performance rates, with an average code coverage rate of 98.1%, a total of 3500 vulnerabilities detected, a vulnerability detection rate of 98.7%, a false positive rate of 1.3%, a recall of 98.2%, precision of 98.8%, a path uniqueness rate of 96.4%, false negative rate of 3.5%, an execution time of 25 s, and test case generation time of 16 s. In conclusion, our proposed approach demonstrates a significant improvement over existing methods for test case generation by providing a promising solution for the robustness of smart contracts and security enhancement in the DeFi ecosystem.