Enhancing Fault Detection in Smart Contract Loops Through Adaptive Probabilistic Sampling

Abstract

Smart contracts are programs that reside on a block-chain. A key feature of smart contracts is their immutability, meaning that they cannot be modified once they are deployed. Despite existing efforts to uncover vulnerabilities, a common assumption is that loop structures rarely occur in smart contracts. Traditional search-based algorithms encounter challenges in uniform exploration, particularly in complex control flow paths where certain paths are more critical than others for fault detection. To overcome this challenge, we propose an adaptive probabilistic sampling strategy that allows for targeted exploration of critical paths. The proposed adaptive probabilistic sampling strategy empowers search-based algorithms to select individuals covering paths with defects. Experimental results demonstrate the efficacy of the proposed adaptive probabilistic sampling strategy in ensuring targeted exploration of critical paths within smart contract loops and enhanced mutant-killing capabilities when combined with search-based algorithms.