Towards an assessment grid for intelligent modeling assistance

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.

[1]  Antonio Vallecillo,et al.  Belief Uncertainty in Software Models , 2019, 2019 IEEE/ACM 11th International Workshop on Modelling in Software Engineering (MiSE).

[2]  Yves Le Traon,et al.  The next evolution of MDE: a seamless integration of machine learning into domain modeling , 2017, 2017 ACM/IEEE 20th International Conference on Model Driven Engineering Languages and Systems (MODELS).

[3]  Raja Parasuraman,et al.  Humans and Automation: Use, Misuse, Disuse, Abuse , 1997, Hum. Factors.

[4]  Pekka Toivanen,et al.  Harmonization and Categorization of Metrics and Criteria for Evaluation of Recommender Systems in Healthcare From Dual Perspectives , 2020, Int. J. E Health Medical Commun..

[5]  Dong Liu,et al.  UCDA: Use Case Driven Development Assistant Tool for Class Model Generation , 2004, SEKE.

[6]  Patrick Mäder,et al.  Pattern-based auto-completion of UML modeling activities , 2014, ASE.

[7]  Kay Römer,et al.  Improving the Timeliness of Bluetooth Low Energy in Dynamic RF Environments , 2020, ACM Trans. Internet Things.

[8]  Hanêne Ben-Abdallah,et al.  An UML class recommender system for software design , 2016, 2016 IEEE/ACS 13th International Conference of Computer Systems and Applications (AICCSA).

[9]  Zarinah Mohd Kasirun,et al.  Feature extraction approaches from natural language requirements for reuse in software product lines: A systematic literature review , 2015, J. Syst. Softw..

[10]  A. V. Bogatyrev,et al.  The Probability of Timeliness of a Fully Connected Exchange in a Redundant Real-Time Communication System , 2020, 2020 Wave Electronics and its Application in Information and Telecommunication Systems (WECONF).

[11]  Richard F. Paige,et al.  Grand challenges in model-driven engineering: an analysis of the state of the research , 2020, Software and Systems Modeling.

[12]  Sammy W. Pearson,et al.  Development of a Tool for Measuring and Analyzing Computer User Satisfaction , 1983 .

[13]  Nelly Bencomo,et al.  Non-human Modelers: Challenges and Roadmap for Reusable Self-explanation , 2017, STAF Workshops.

[14]  Arne Sølvberg,et al.  Understanding quality in conceptual modeling , 1994, IEEE Software.

[15]  Ralf-Detlef Kutsche,et al.  DoMoRe - A Recommender System for Domain Modeling , 2018, MODELSWARD.

[16]  Houari A. Sahraoui,et al.  Multi-Step Learning and Adaptive Search for Learning Complex Model Transformations from Examples , 2016, ACM Trans. Softw. Eng. Methodol..

[17]  Hans Vangheluwe,et al.  Towards Domain-specific Model Editors with Automatic Model Completion , 2010, Simul..

[18]  Ingo J. Timm,et al.  Autonomy in Software Systems , 2007 .

[19]  Juan de Lara,et al.  Towards Automating the Synthesis of Chatbots for Conversational Model Query , 2020, BPMDS/EMMSAD@CAiSE.

[20]  Mark Rouncefield,et al.  The State of Practice in Model-Driven Engineering , 2014, IEEE Software.

[21]  Juan de Lara,et al.  Collaborative Modeling and Group Decision Making Using Chatbots in Social Networks , 2018, IEEE Software.

[22]  Jan Mendling,et al.  Process Model Generation from Natural Language Text , 2011, CAiSE.

[23]  João Paulo Papa,et al.  How Far You Can Get Using Machine Learning Black-Boxes , 2010, 2010 23rd SIBGRAPI Conference on Graphics, Patterns and Images.

[24]  John Krogstie,et al.  Defining quality aspects for conceptual models , 1995, ISCO.

[25]  Amalina Farhi Ahmad Fadzlah,et al.  TIMELINESS MEASUREMENT MODEL: A MATHEMATICAL APPROACH FORMEASURING THE TIMELINESS OF HANDHELD APPLICATION USAGE , 2012 .

[26]  Jörg Kienzle,et al.  Toward model-driven sustainability evaluation , 2020, Commun. ACM.

[27]  Yves Le Traon,et al.  The Next Evolution of MDE: A Seamless Integration of Machine Learning into Domain Modeling , 2017, MoDELS.

[28]  Jörg Kienzle,et al.  A Hitchhiker's Guide to Model-Driven Engineering for Data-Centric Systems , 2020, IEEE Software.

[29]  Antonio Vallecillo,et al.  Expressing Confidence in Models and in Model Transformation Elements , 2018, MoDELS.

[30]  Sébastien Gérard,et al.  An LSTM-Based Neural Network Architecture for Model Transformations , 2019, 2019 ACM/IEEE 22nd International Conference on Model Driven Engineering Languages and Systems (MODELS).

[31]  Stefan Kögel,et al.  Recommender system for model driven software development , 2017, ESEC/SIGSOFT FSE.

[32]  Hong Zhu,et al.  Empirical validation of Website timeliness measures , 2005, 29th Annual International Computer Software and Applications Conference (COMPSAC'05).

[33]  Rodina Ahmad,et al.  Class Diagram Extraction from Textual Requirements Using Natural Language Processing (NLP) Techniques , 2010, 2010 Second International Conference on Computer Research and Development.

[34]  Haoxiang Wang,et al.  TEAN: Timeliness enhanced attention network for session-based recommendation , 2020, Neurocomputing.

[35]  Benoît Combemale,et al.  The Relevance of Model-Driven Engineering Thirty Years from Now , 2014, MoDELS.