ArticleSoftware and systems modeling2022
AI-driven streamlined modeling: experiences and lessons learned from multiple domains.
Article in Software and systems modeling, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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4 authors.
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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.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.