A Survey on Trends and Challenges in AI-Powered Smart Contract Analysis

Abstract

Smart contracts are the backbone of decentralized applications, enabling secure and autonomous execution of digital contracts on blockchain platforms. However, increasing complexity and immutability of these contracts are causing severe security threats. Traditional auditing techniques, although helpful, are limited in scalability and mostly incapable of detecting emerging vulnerabilities. This has led to a growing desire to extend the deployment of Artificial Intelligence (AI) and Natural Language Processing (NLP) techniques to the audit and security of smart contracts. In this paper, we present a comprehensive review of AI-based approaches of auditing and securing smart contracts, highlighting recent advances in machine learning, deep learning, and transformer-based architectures. We discuss stateof-the-art tools and frameworks, compare their methodology, and outline their respective strengths and weaknesses. We also discuss important challenges like availability of datasets, generalization to unknown vulnerabilities, interpretability of AI results, and integration with existing blockchain platforms. Finally, we discuss future research directions and propose future work for the development of more secure, intelligent, and scalable systems for analyzing smart contracts.