Paper Info
Title
Towards an assessment grid for intelligent modeling assistance
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.
Year
DOI
Venue
2020
10.1145/3417990.3421396
MODELS '20: ACM/IEEE 23rd International Conference on Model Driven Engineering Languages and Systems Virtual Event Canada October, 2020
DocType
Volume
ISBN
Conference
10
978-1-4503-8135-2
Citations 
PageRank 
References 
0
0.34
0
Authors
11
Name
Order
Citations
PageRank
Gunter Mussbacher1129.02
Benoît Combemale242346.61
Silvia Abrahão350.76
Nelly Bencomo4195097.71
Loli Burgueño514620.64
Gregor Engels62245420.50
Jörg Kienzle773269.38
Thomas Kühn800.34
Sébastien Mosser924725.15
Houari Sahraoui1080142.47
Martin Weyssow1141.45