NLP for Smart Contracts

Apati, Nicholas Chukwudi (2024) NLP for Smart Contracts. Masters thesis, Dublin, National College of Ireland.

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Abstract

This research presents a way to determine how well an NLP algorithm can translate smart contracts that are in Solidity programming language to natural language that people can comprehend and vice versa. The approach is centered on enabling the improvements of smart contracts’ interpretability and scalability by applying NLP in the cryptocurrency context. It is possible to conclude that the goal of the discussed study is to introduce NLP algorithms into decision-making processes in smart contracts in a way that would be transparent and open to users. In the method, the literature research, the NLP algorithm for smart contract analysis and generation, and experiments to estimate the efficiency of the algorithms are described. A front-end HTML interface has been created; an API key has been created through OpenAI for translating Solidity code into English and back. Some experiments prove that the interface can take the translations, but to resolve present issues, optimisation is required. The expected outcomes include comprehending how NLP will be employed to translate smart contracts and how these contracts can be developed and enforced in the Case of Cryptocurrencies.

Item Type:	Thesis (Masters)
Supervisors:	Name Email Del Rosal, Victor UNSPECIFIED
Subjects:	Q Science > QH Natural history > QH301 Biology > Methods of research. Technique. Experimental biology > Data processing. Bioinformatics > Artificial intelligence Q Science > Q Science (General) > Self-organizing systems. Conscious automata > Artificial intelligence H Social Sciences > HG Finance > Money > Digital currency H Social Sciences > HG Finance > Fintech T Technology > T Technology (General) > Information Technology > Fintech
Divisions:	School of Computing > Master of Science in FinTech
Depositing User:	Ciara O'Brien
Date Deposited:	02 Aug 2025 13:24
Last Modified:	02 Aug 2025 13:24
URI:	https://norma.ncirl.ie/id/eprint/8414

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