VortexDraft: A blockchain smart contract auto-generation system based on named entity recognition

As blockchain technology continues to evolve, its inherent decentralization, transparency, and immutability offer increasingly viable applications for smart contracts in the financial sector. Smart contracts autonomously execute and enforce transaction terms without the need for third-party intermediaries, thus enhancing both transaction efficiency and security. However, the creation of smart contracts generally requires advanced programming expertise, limiting their accessibility to non-technical users. To address this challenge, this paper introduces VortexDraft, an automated smart contract generation system leveraging Named Entity Recognition (NER). The system employs a user-configurable NER dataset to train a BERT (Bidirectional Encoder Representations from Transformers) model, facilitating the accurate extraction of key information from natural language inputs, which is then automatically translated into Solidity-compliant smart contract code. Experimental evaluations demonstrate that VortexDraft achieves over 99% accuracy in NER tasks, with the generated Solidity code aligning closely with the specifications outlined in natural language descriptions. This significantly enhances the efficiency and precision of smart contract generation, offering a robust solution for decentralized finance and other application areas.

Authors

Institutions

Publication Details

Journal
IET conference proceedings.
Published
2025-01-09
DOI
https://doi.org/10.1049/icp.2024.4250
Primary Topic
Blockchain Technology Applications and Security
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

VortexDraft: A blockchain smart contract auto-generation system based on named entity recognition

Yan Kai, Jiunn-Ru Lai
IET conference proceedings.
Blockchain Technology Applications and Security
article

VortexDraft: A blockchain smart contract auto-generation system based on named entity recognition

Yan Kai, Jiunn-Ru Lai
article en

Abstract

As blockchain technology continues to evolve, its inherent decentralization, transparency, and immutability offer increasingly viable applications for smart contracts in the financial sector. Smart contracts autonomously execute and enforce transaction terms without the need for third-party intermediaries, thus enhancing both transaction efficiency and security. However, the creation of smart contracts generally requires advanced programming expertise, limiting their accessibility to non-technical users. To address this challenge, this paper introduces VortexDraft, an automated smart contract generation system leveraging Named Entity Recognition (NER). The system employs a user-configurable NER dataset to train a BERT (Bidirectional Encoder Representations from Transformers) model, facilitating the accurate extraction of key information from natural language inputs, which is then automatically translated into Solidity-compliant smart contract code. Experimental evaluations demonstrate that VortexDraft achieves over 99% accuracy in NER tasks, with the generated Solidity code aligning closely with the specifications outlined in natural language descriptions. This significantly enhances the efficiency and precision of smart contract generation, offering a robust solution for decentralized finance and other application areas.

IET conference proceedings.Vol. 2024(21)
Foshan University (CN)
Openalex Percentile: Top 100%
Blockchain Technology Applications and Security
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.