research-article

Towards an assessment grid for intelligent modeling assistance

Authors:

Gunter Mussbacher,

Benoit Combemale,

Silvia Abrahão,

Loli Burgueño,

Sébastien Mosser,

Houari Sahraoui,

Martin WeyssowAuthors Info & Claims

MODELS '20: Proceedings of the 23rd ACM/IEEE International Conference on Model Driven Engineering Languages and Systems: Companion Proceedings

Article No.: 48, Pages 1 - 10

https://doi.org/10.1145/3417990.3421396

Published: 26 October 2020 Publication History

Abstract

The ever-growing complexity of systems, the growing number of stakeholders, and the corresponding continuous emergence of new domain-specific modeling abstractions has led to significantly higher cognitive load on modelers. There is an urgent need to provide modelers with better, more Intelligent Modeling Assistants (IMAs). An important factor to consider is the ability to assess and compare, to learn from existing and inform future IMAs, while potentially combining them. Recently, a conceptual Reference Framework for Intelligent Modeling Assistance (RF-IMA) was proposed. RF-IMA defines the main required components and high-level properties of IMAs. In this paper, we present a detailed, level-wise definition for the properties of RF-IMA to enable a better understanding, comparison, and selection of existing and future IMAs. The proposed levels are a first step towards a comprehensive assessment grid for intelligent modeling assistance. For an initial validation of the proposed levels, we assess the existing landscape of intelligent modeling assistance and three future scenarios of intelligent modeling assistance against these levels.

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Cited By

Pfeiffer JFuchß DKühn TLiebhart RNeumann DNeimöck CSeiler CKoziolek AWortmann AEgyed AWimmer MChechik MCombemale B(2024)Modeling Languages for Automotive Digital Twins: A Survey Among the German Automotive IndustryProceedings of the ACM/IEEE 27th International Conference on Model Driven Engineering Languages and Systems10.1145/3640310.3674100(92-103)Online publication date: 22-Sep-2024
https://dl.acm.org/doi/10.1145/3640310.3674100
Oakes BFamelis MSahraoui H(2023)Building Domain-Specific Machine Learning Workflows: A Conceptual Framework for the State of the PracticeACM Transactions on Software Engineering and Methodology10.1145/363824333:4(1-50)Online publication date: 21-Dec-2023
https://dl.acm.org/doi/10.1145/3638243
Mora Segura Áde Lara JWimmer M(2023)Modelling assistants based on information reuse: a user evaluation for language engineeringSoftware and Systems Modeling10.1007/s10270-023-01094-523:1(57-84)Online publication date: 17-Apr-2023
https://doi.org/10.1007/s10270-023-01094-5
Show More Cited By

Index Terms

Towards an assessment grid for intelligent modeling assistance
1. General and reference
  1. Cross-computing tools and techniques
    1. Evaluation
2. Software and its engineering
  1. Software notations and tools
    1. Development frameworks and environments
      1. Application specific development environments
      2. Integrated and visual development environments
  2. Software organization and properties
    1. Software system structures
      1. Abstraction, modeling and modularity
      2. Software system models
        Model-driven software engineering

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cover image ACM Conferences

MODELS '20: Proceedings of the 23rd ACM/IEEE International Conference on Model Driven Engineering Languages and Systems: Companion Proceedings

October 2020

713 pages

ISBN:9781450381352

DOI:10.1145/3417990

Conference Chairs:
Esther Guerra
Universidad Autonoma de Madrid, Spain
,
Ludovico Iovino
Gran Sasso Science Institute, Italy

Copyright © 2020 ACM.

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Published: 26 October 2020

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MODELS '20: ACM/IEEE 23rd International Conference on Model Driven Engineering Languages and Systems

October 16 - 23, 2020

Virtual Event, Canada

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Overall Acceptance Rate 118 of 382 submissions, 31%

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Cited By

Pfeiffer JFuchß DKühn TLiebhart RNeumann DNeimöck CSeiler CKoziolek AWortmann AEgyed AWimmer MChechik MCombemale B(2024)Modeling Languages for Automotive Digital Twins: A Survey Among the German Automotive IndustryProceedings of the ACM/IEEE 27th International Conference on Model Driven Engineering Languages and Systems10.1145/3640310.3674100(92-103)Online publication date: 22-Sep-2024
https://dl.acm.org/doi/10.1145/3640310.3674100
Oakes BFamelis MSahraoui H(2023)Building Domain-Specific Machine Learning Workflows: A Conceptual Framework for the State of the PracticeACM Transactions on Software Engineering and Methodology10.1145/363824333:4(1-50)Online publication date: 21-Dec-2023
https://dl.acm.org/doi/10.1145/3638243
Mora Segura Áde Lara JWimmer M(2023)Modelling assistants based on information reuse: a user evaluation for language engineeringSoftware and Systems Modeling10.1007/s10270-023-01094-523:1(57-84)Online publication date: 17-Apr-2023
https://doi.org/10.1007/s10270-023-01094-5
Adhikari BRapos EStephan M(2023)SimIMA: a virtual Simulink intelligent modeling assistantSoftware and Systems Modeling10.1007/s10270-023-01093-623:1(29-56)Online publication date: 13-Mar-2023
https://doi.org/10.1007/s10270-023-01093-6
Bergelin JStrandberg PKühn TSousa V(2022)Industrial requirements for supporting AI-enhanced model-driven engineeringProceedings of the 25th International Conference on Model Driven Engineering Languages and Systems: Companion Proceedings10.1145/3550356.3561609(375-379)Online publication date: 23-Oct-2022
https://dl.acm.org/doi/10.1145/3550356.3561609
Di Rocco JDi Ruscio DDi Sipio CNguyen PPierantonio A(2022)MemoRec: a recommender system for assisting modelers in specifying metamodelsSoftware and Systems Modeling10.1007/s10270-022-00994-222:1(203-223)Online publication date: 29-Mar-2022
https://doi.org/10.1007/s10270-022-00994-2
Sunkle SSaxena KPatil AKulkarni V(2022)AI-driven streamlined modeling: experiences and lessons learned from multiple domainsSoftware and Systems Modeling10.1007/s10270-022-00982-621:3(1-23)Online publication date: 19-Feb-2022
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Weyssow MSahraoui HSyriani E(2022)Recommending metamodel concepts during modeling activities with pre-trained language modelsSoftware and Systems Modeling10.1007/s10270-022-00975-521:3(1071-1089)Online publication date: 12-Feb-2022
https://doi.org/10.1007/s10270-022-00975-5
Almonte LGuerra ECantador Ide Lara J(2021)Recommender systems in model-driven engineeringSoftware and Systems Modeling10.1007/s10270-021-00905-xOnline publication date: 26-Jul-2021
https://doi.org/10.1007/s10270-021-00905-x

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