TY - GEN
T1 - Towards an assessment grid for intelligent modeling assistance
AU - Mussbacher, Gunter
AU - Combemale, Benoit
AU - Abrahão, Silvia
AU - Bencomo, Nelly
AU - Burgueño, Loli
AU - Engels, Gregor
AU - Kienzle, Jörg
AU - Kühn, Thomas
AU - Mosser, Sébastien
AU - Sahraoui, Houari
AU - Weyssow, Martin
PY - 2020/10/16
Y1 - 2020/10/16
N2 - 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.
AB - 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.
KW - Artificial intelligence
KW - Assessment levels
KW - Feedback
KW - Integrated development environment
KW - Intelligent modeling assistance
KW - Model-based software engineering
UR - https://dl.acm.org/doi/10.1145/3417990.3421396
UR - http://www.scopus.com/inward/record.url?scp=85096779067&partnerID=8YFLogxK
U2 - 10.1145/3417990.3421396
DO - 10.1145/3417990.3421396
M3 - Conference publication
T3 - Proceedings - 23rd ACM/IEEE International Conference on Model Driven Engineering Languages and Systems, MODELS-C 2020 - Companion Proceedings
SP - 1
EP - 10
BT - Proceedings - 23rd ACM/IEEE International Conference on Model Driven Engineering Languages and Systems, MODELS-C 2020 - Companion Proceedings
PB - ACM
ER -