A visual editor for marked-up codes with auto-completion

Trevor Po; Leander D'Souza; Stefan Fuchs; Robert Amor

doi:10.17868/strath.00093267

A visual editor for marked-up codes with auto-completion

Trevor Po
, Leander D'Souza
, Stefan Fuchs
, Robert Amor

Architecture

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution book

7 Downloads (Pure)

Abstract

Manual translation of codes and standards into a marked-up and computerised form is not well supported for the code experts who would be expected to undertake this task. This research examines development of a visual LegalRuleML editor built with a deep learning model trained on legal clauses to automate manual translation tasks with intelligent auto-completion. Furthermore, this research aims to evaluate if visual programming languages can provide a viable and user-friendly LegalRuleML representation and whether visual auto-completion with deep learning is an effective feature to streamline the workflow when translating legal clauses. A user study identified that the implemented visual editor provided a user-friendly and intuitive interface that successfully aided users with no prior knowledge within the field in learning and applying LegalRuleML constructs more efficiently.

Original language	English
Title of host publication	EG-ICE 2025
Subtitle of host publication	AI-Driven Collaboration for Sustainable and Resilient Built Environments Conference Proceedings
Editors	Alejandro Moreno-Rangel, Bimal Kumar
Place of Publication	Glasgow
Pages	39-48
Number of pages	10
DOIs	https://doi.org/10.17868/strath.00093267
Publication status	Published - 1 Jul 2025
Event	EG-ICE 2025: International Workshop on Intelligent Computing in Engineering - The Technology and Innovation Centre, Glasgow, United Kingdom Duration: 1 Jul 2025 → 3 Jul 2025 https://egice2025.co.uk/

Conference

Conference	EG-ICE 2025: International Workshop on Intelligent Computing in Engineering
Country/Territory	United Kingdom
City	Glasgow
Period	1/07/25 → 3/07/25
Internet address	https://egice2025.co.uk/

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 11 Sustainable Cities and Communities

Keywords

visual editor
LegalRuleML
auto-completion
mark-up
codes

Access to Document

10.17868/strath.00093267Licence: CC BY 4.0

Po-etal-EG-ICE-2025-A-visual-editor-for-marked-up-codesFinal published version, 1.06 MBLicence: CC BY 4.0

1 Book

EG-ICE 2025: AI-Driven Collaboration for Sustainable and Resilient Built Environments Conference Proceedings
Moreno-Rangel, A. & Kumar, B., 6 Mar 2026, Glasgow.
Research output: Book/Report › Book

Open Access
File

23 Downloads (Pure)

Cite this

@inproceedings{5dc7e6089338483fbb547700f712ba92,

title = "A visual editor for marked-up codes with auto-completion",

abstract = "Manual translation of codes and standards into a marked-up and computerised form is not well supported for the code experts who would be expected to undertake this task. This research examines development of a visual LegalRuleML editor built with a deep learning model trained on legal clauses to automate manual translation tasks with intelligent auto-completion. Furthermore, this research aims to evaluate if visual programming languages can provide a viable and user-friendly LegalRuleML representation and whether visual auto-completion with deep learning is an effective feature to streamline the workflow when translating legal clauses. A user study identified that the implemented visual editor provided a user-friendly and intuitive interface that successfully aided users with no prior knowledge within the field in learning and applying LegalRuleML constructs more efficiently. ",

keywords = "visual editor, LegalRuleML, auto-completion, mark-up, codes",

author = "Trevor Po and Leander D'Souza and Stefan Fuchs and Robert Amor",

year = "2025",

month = jul,

day = "1",

doi = "10.17868/strath.00093267",

language = "English",

isbn = "9781914241826",

pages = "39--48",

editor = "Alejandro Moreno-Rangel and Bimal Kumar",

booktitle = "EG-ICE 2025",

note = "EG-ICE 2025: International Workshop on Intelligent Computing in Engineering ; Conference date: 01-07-2025 Through 03-07-2025",

url = "https://egice2025.co.uk/",

}

Po, T, D'Souza, L, Fuchs, S & Amor, R 2025, A visual editor for marked-up codes with auto-completion. in A Moreno-Rangel & B Kumar (eds), EG-ICE 2025: AI-Driven Collaboration for Sustainable and Resilient Built Environments Conference Proceedings. Glasgow, pp. 39-48, EG-ICE 2025: International Workshop on Intelligent Computing in Engineering, Glasgow, United Kingdom, 1/07/25. https://doi.org/10.17868/strath.00093267

A visual editor for marked-up codes with auto-completion. / Po, Trevor; D'Souza, Leander; Fuchs, Stefan et al.
EG-ICE 2025: AI-Driven Collaboration for Sustainable and Resilient Built Environments Conference Proceedings. ed. / Alejandro Moreno-Rangel; Bimal Kumar. Glasgow, 2025. p. 39-48.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution book

TY - GEN

T1 - A visual editor for marked-up codes with auto-completion

AU - Po, Trevor

AU - D'Souza, Leander

AU - Fuchs, Stefan

AU - Amor, Robert

PY - 2025/7/1

Y1 - 2025/7/1

N2 - Manual translation of codes and standards into a marked-up and computerised form is not well supported for the code experts who would be expected to undertake this task. This research examines development of a visual LegalRuleML editor built with a deep learning model trained on legal clauses to automate manual translation tasks with intelligent auto-completion. Furthermore, this research aims to evaluate if visual programming languages can provide a viable and user-friendly LegalRuleML representation and whether visual auto-completion with deep learning is an effective feature to streamline the workflow when translating legal clauses. A user study identified that the implemented visual editor provided a user-friendly and intuitive interface that successfully aided users with no prior knowledge within the field in learning and applying LegalRuleML constructs more efficiently.

AB - Manual translation of codes and standards into a marked-up and computerised form is not well supported for the code experts who would be expected to undertake this task. This research examines development of a visual LegalRuleML editor built with a deep learning model trained on legal clauses to automate manual translation tasks with intelligent auto-completion. Furthermore, this research aims to evaluate if visual programming languages can provide a viable and user-friendly LegalRuleML representation and whether visual auto-completion with deep learning is an effective feature to streamline the workflow when translating legal clauses. A user study identified that the implemented visual editor provided a user-friendly and intuitive interface that successfully aided users with no prior knowledge within the field in learning and applying LegalRuleML constructs more efficiently.

KW - visual editor

KW - LegalRuleML

KW - auto-completion

KW - mark-up

KW - codes

U2 - 10.17868/strath.00093267

DO - 10.17868/strath.00093267

M3 - Conference contribution book

SN - 9781914241826

SP - 39

EP - 48

BT - EG-ICE 2025

A2 - Moreno-Rangel, Alejandro

A2 - Kumar, Bimal

CY - Glasgow

T2 - EG-ICE 2025: International Workshop on Intelligent Computing in Engineering

Y2 - 1 July 2025 through 3 July 2025

ER -

A visual editor for marked-up codes with auto-completion

Abstract

Conference

UN SDGs

Keywords

Access to Document

Fingerprint

Research output

EG-ICE 2025: AI-Driven Collaboration for Sustainable and Resilient Built Environments Conference Proceedings

Cite this