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Software and Systems Modeling
Erschienen in: Software and Systems Modeling 3/2022

19.02.2022 | Theme Section Paper

AI-driven streamlined modeling: experiences and lessons learned from multiple domains

verfasst von: Sagar Sunkle, Krati Saxena, Ashwini Patil, Vinay Kulkarni

Erschienen in: Software and Systems Modeling | Ausgabe 3/2022

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Abstract

Model-driven technologies (MD*), considered beneficial through abstraction and automation, have not enjoyed widespread adoption in the industry. In keeping with the recent trends, using AI techniques might help the benefits of MD* outweigh their costs. Although the modeling community has started using AI techniques, it is, in our opinion, quite limited and requires a change in perspective. We provide such a perspective through five industrial case studies where we use AI techniques in different modeling activities. We discuss our experiences and lessons learned, in some cases evolving purely modeling solutions with AI techniques, and in others considering the AI aids from the beginning. We believe that these case studies can help the researchers and practitioners make sense of various artifacts and data available to them and use applicable AI techniques to enhance suitable modeling activities.
Vorheriger Artikel Automated, interactive, and traceable domain modelling empowered by artificial intelligence
Nächster Artikel Recommending metamodel concepts during modeling activities with pre-trained language models

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Fußnoten
1
The two papers have massive citation counts, more than 36K for [45] published in 2015, and more than 16K for [27] published in 2018, indicating an extensive outreach and increasing use of DL and, in general, AI techniques in every human endeavor.
 
2
Language Models are Few-Shot Learners https://​github.​com/​openai/​gpt-3.
 
11
MiFID- Markets in Financial Instruments Directive (2004/39/EC) https://​www.​esma.​europa.​eu/​policy-rules/​mifid-ii-and-mifir.
 
18
CMG deliberately takes such a broad-based approach as explained in [77, 78].
 
20
See Spacy Dutch language model https://​spacy.​io/​models/​nl.
 
22
An example of such an alternate dataset is the COVID-19 Open Research Dataset at https://​www.​kaggle.​com/​allen-institute-for-ai/​CORD-19-research-challenge, which has been prepared using over 400,000 scholarly articles, including over 150,000 with full text, about COVID-19, SARS-CoV-2, and related coronaviruses.
 
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Metadaten
Titel
AI-driven streamlined modeling: experiences and lessons learned from multiple domains
verfasst von
Sagar Sunkle
Krati Saxena
Ashwini Patil
Vinay Kulkarni
Publikationsdatum
19.02.2022
Verlag
Springer Berlin Heidelberg
Erschienen in
Software and Systems Modeling / Ausgabe 3/2022
Print ISSN: 1619-1366
Elektronische ISSN: 1619-1374
DOI
https://doi.org/10.1007/s10270-022-00982-6

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