This paper introduces a user-friendly tool designed to simplify smart contract analysis through a microservice architecture implemented in Golang and Flutter. The system combines four primary components: a Codec for contract encoding/decoding, an Auditor powered by Slither for security assessment, a Database for version control, and an AI Assistant leveraging gpt-3.5-turbo from OpenAI for automated annotations. User interaction is facilitated through UML-based visual representations that allow intuitive comprehension of the smart contract implementation. An evaluation through the NASA-TLX questionnaire demonstrated that the proposed tool could significantly reduce the overall workload (52.62 versus 71.74) compared to Remix IDE, with notable improvements in mental demand, temporal demand, and frustration levels.

In the rapidly evolving landscape of blockchain technology, smart contracts stand as pivotal instrument for automating and enforcing digital agreements. However, their creation often necessitates specialized programming skills, hindering broader adoption and accessibility. This paper proposes a pipeline that leverages the capabilities of Large Language Models (LLMs) to automate the generation of smart contracts. By harnessing the natural language understanding and generation capabilities of LLMs, our approach aims to make accessible smart contract development to people that are not familiar with this task. The proposed pipeline employs the CO-STAR methodology to optimize prompt creation for high-quality outputs. Moreover, in order to assess the correctness and reliability of the generated smart contracts, we leverage on Slither, one of the most cutting-edge vulnerability detection tools. Furthermore, we propose a benchmarking suite based on metrics such as compilability, vulnerabilities, and presence of comments, among the others, in order to evaluate the effectiveness of the pipeline in terms of consistency of generated smart contracts, LLM's temperature effect, and prompt selection. The results show that our pipeline is able to produce 98.1% of compilable smart contracts, the temperature value has negligible effect on the generated smart contracts, and the CO-STAR methodology produces valuable and consistent outputs with low-impact vulnerabilities.

The growing number of exploits and hacks on the Ethereum blockchain has led to the development of powerful smart contract vulnerability detection tools and the frequent patching of the smart contract’s programming languages (such as Solidity). At the same time, an ever-increasing number of people are interested in blockchain and smart contract-related topics and willing to build and deploy their own Decentralized Applications (dApp). However, learning a new programming language and its best practices as long as how to actually deploy a smart contract on the blockchain is a difficult task even for experienced developers. Recently, ChatGPT, a new user-friendly deep learning tool, has been released to improve the ability of non-skilled users to write high-quality code and in general, to boost the performances of developers in key tasks related to code writing (i.e., writing functions, explaining runtime errors, fixing bugs, etc.). This paper aims to measure the capabilities of ChatGPT in fixing vulnerable smart contracts and to assess the effectiveness of this tool, determining whether it can be a valuable aid for those who want to correct their own smart contract or want to reuse existing ones by first checking their status and eventually fix their vulnerability. In particular, we asked ChatGPT to fix 143 smart contracts with well-known labeled vulnerabilities. We considered a vulnerability as "fixed" if the code corrected by ChatGPT no longer contained the vulnerability (for this purpose, we exploited Slither, one of the state-of-the-art tools for smart contracts vulnerability detection to check the status of the original and the corrected smart contracts). As a result we obtained that ChatGPT was able to fix bugs and vulnerable smart contracts on average the 57.1% of the time with an increase of +1.4% when a description of the bug was provided in addition to the smart contract’s source code.