Models2Code: Autonomous model‐based generation to expedite the engineering process

Abstract Insufficient resources and high costs are hindering industrial development, potentially impeding adaptation to market demands. Overcoming this challenge necessitates advancements in software engineering techniques to streamline processes and meet industrial requirements. Crucially, automating manual tasks and enhancing interoperability between engineering stages can yield efficiency gains. This paper presents a model‐based system engineering approach aimed at automating the transition from design to implementation, incorporating autonomous generation and validation features. Implemented as plugins and utilizing model transformation techniques, this solution targets reducing engineering time and facilitating the adoption of new technologies. Developed, implemented, and tested within the Arrowhead framework, the approach is followed by a discussion on its benefits and limitations.

Authors

Institutions

Publication Details

Journal
Systems Engineering
Published
2024-10-28
DOI
https://doi.org/10.1002/sys.21789
Citations
3
Primary Topic
Manufacturing Process and Optimization
Type
article
Field-Weighted Citation Impact
1.05

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Models2Code: Autonomous model‐based generation to expedite the engineering process

Fernando Labra Caso, Cristina Paniagua
3 citations
Systems Engineering
Manufacturing Process and Optimization
1.05
article

Models2Code: Autonomous model‐based generation to expedite the engineering process

Fernando Labra Caso, Cristina Paniagua
article en
3 citations

Abstract

Abstract Insufficient resources and high costs are hindering industrial development, potentially impeding adaptation to market demands. Overcoming this challenge necessitates advancements in software engineering techniques to streamline processes and meet industrial requirements. Crucially, automating manual tasks and enhancing interoperability between engineering stages can yield efficiency gains. This paper presents a model‐based system engineering approach aimed at automating the transition from design to implementation, incorporating autonomous generation and validation features. Implemented as plugins and utilizing model transformation techniques, this solution targets reducing engineering time and facilitating the adoption of new technologies. Developed, implemented, and tested within the Arrowhead framework, the approach is followed by a discussion on its benefits and limitations.

Systems EngineeringVol. 28(2)
Luleå University of Technology (SE)
European Commission
Industry, innovation and infrastructure
Openalex Percentile: Top 20%
Manufacturing Process and Optimization
1.05
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.

Models2Code: Autonomous model‐based generation to expedite the engineering process — Fernando Labra Caso, Cristina Paniagua · Systems Engineering (2024) | TGRS Research Map | TGRS