research-article

Towards an assessment grid for intelligent modeling assistance

Authors:

Gunter Mussbacher,

Benoit Combemale,

Silvia Abrahão,

Loli Burgueño,

Sébastien Mosser,

Houari Sahraoui,

Martin WeyssowAuthors Info & Claims

MODELS '20: Proceedings of the 23rd ACM/IEEE International Conference on Model Driven Engineering Languages and Systems: Companion Proceedings

Article No.: 48, Pages 1 - 10

https://doi.org/10.1145/3417990.3421396

Published: 26 October 2020 Publication History

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.

References

[1]

[n.d.]. Language Server Protocol. https://microsoft.github.io/language-server-protocol/

[2]

[n.d.]. Mendix Assist. https://www.mendix.com/blog/introducing-ai-assisted-development-to-elevate-low-code-platforms-to-the-next-level

[3]

[n.d.]. ServiceStudio from OutSystems. https://www.outsystems.com/ai

[4]

Adekunle Oluseyi Afolabi and Pekka Toivanen. 2020. Harmonization and Categorization of Metrics and Criteria for Evaluation of Recommender Systems in Healthcare From Dual Perspectives. Intl Journal of E-Health and Medical Communications (IJEHMC) 11, 1 (2020), 69--92.

Digital Library

[5]

Henning Agt-Rickauer, Ralf-Detlef Kutsche, and Harald Sack. 2018. DoMoRe - A Recommender System for Domain Modeling. In Proc. of the 6th International Conference on Model-Driven Engineering and Software Development (MODELSWARD'18). 71--82.

Digital Library

[6]

James E. Bailey and Sammy W. Pearson. 1983. Development of a Tool for Measuring and Analyzing Computer User Satisfaction. Management Science 29, 5 (1983), 530--545.

Digital Library

[7]

Noor Hasrina Bakar, Zarinah M. Kasirun, and Norsaremah Salleh. 2015. Feature Extraction Approaches from Natural Language Requirements for Reuse in Software Product Lines: A Systematic Literature Review. J. Syst. Softw. 106, C (Aug. 2015), 132--149.

Digital Library

[8]

Islem Baki and Houari A. Sahraoui. 2016. Multi-Step Learning and Adaptive Search for Learning Complex Model Transformations from Examples. ACM Trans. Softw. Eng. Methodol. 25, 3 (2016), 20:1--20:37.

Digital Library

[9]

A. V. Bogatyrev, V. A. Bogatyrev, and S. V. Bogatyrev. 2020. The Probability of Timeliness of a Fully Connected Exchange in a Redundant Real-Time Communication System. In 2020 Wave Electronics and its Application in Information and Telecommunication Systems (WECONF). IEEE, Saint-Petersburg, Russia, Russia, 1--4.

[10]

Antonio Bucchiarone, Jordi Cabot, Richard Paige, and Alfonso Pierantonio. 2020. Grand challenges in model-driven engineering: an analysis of the state of the research. Software and Systems Modeling (01 2020), 1--9.

[11]

Loli Burgueño, Manuel F. Bertoa, Nathalie Moreno, and Antonio Vallecillo. 2018. Expressing Confidence in Models and in Model Transformation Elements. In Proceedings of the 21th ACM/IEEE International Conference on Model Driven Engineering Languages and Systems (Copenhagen, Denmark) (MODELS '18). Association for Computing Machinery, New York, NY, USA, 57--66.

Digital Library

[12]

Loli Burgueño, Jordi Cabot, and Sébastien Gérard. 2019. An LSTM-Based Neural Network Architecture for Model Transformations. In 2019 ACM/IEEE 22nd International Conference on Model Driven Engineering Languages and Systems (MODELS). IEEE, 294--299.

[13]

Loli Burgueño, Robert Clarisó, Jordi Cabot, Sébastien Gérard, and Antonio Vallecillo. 2019. Belief uncertainty in software models. In Proceedings of the 11th International Workshop on Modelling in Software Engineerings, MiSE@ICSE 2019, Montreal, QC, Canada, May 26-27, 2019, Marsha Chechik, Daniel Strüber, and Daniel Varró (Eds.). ACM, 19--26.

Digital Library

[14]

Dongpei Chen, Xingming Zhang, Haoxiang Wang, and Weina Zhang. 2020. TEAN: Timeliness enhanced attention network for session-based recommendation. Neurocomputing 411 (2020), 229 -- 238. htttps://

[15]

Benoit Combemale, Jörg Kienzle, Gunter Mussbacher, Hyacinth Ali, Daniel Amyot, and et al. 2020. A Hitchhiker's Guide to Model-Driven Engineering for Data-Centric Systems. IEEE Software (2020).

[16]

A. Elkamel, M. Gzara, and H. Ben-Abdallah. 2016. An UML class recommender system for software design. In 2016 IEEE/ACS 13th International Conference of Computer Systems and Applications (AICCSA). 1--8.

[17]

Amalina Farhi Ahmad Fadzlah. 2012. Timeliness Measurement Model: A mathematical approach for measuring the timeliness of handheld application usage. International Journal of New Computer Architectures and their Applications (IJNCAA) 2, 3 (2012), 431--450. https://go.gale.com/ps/anonymous?id=GALE|A353645948

[18]

Fabian Friedrich, Jan Mendling, and Frank Puhlmann. 2011. Process model generation from natural language text. In International Conference on Advanced Information Systems Engineering (CAISE). Springer, 482--496.

[19]

Antonio Garcia-Dominguez and Nelly Bencomo. 2018. Non-human Modelers: Challenges and Roadmap for Reusable Self-explanation. In Software Technologies: Applications and Foundations, Martina Seidl and Steffen Zschaler (Eds.). Springer International Publishing, Cham, 161--171.

[20]

T. Hartmann, A. Moawad, F. Fouquet, and Y. Le Traon. 2017. The Next Evolution of MDE: A Seamless Integration of Machine Learning into Domain Modeling. In 2017 ACM/IEEE 20th International Conference on Model Driven Engineering Languages and Systems (MODELS). 180--180.

Digital Library

[21]

M. Ibrahim and R. Ahmad. 2010. Class Diagram Extraction from Textual Requirements Using Natural Language Processing (NLP) Techniques. In Proc. of the 2nd International Conference on Computer Research and Development. 200--204.

Digital Library

[22]

Jörg Kienzle, Gunter Mussbacher, Benoit Combemale, Lucy Bastin, Nelly Bencomo, and et al. 2020. Towards Model-Driven Sustainability Evaluation. Commun. ACM 63, 3 (1 3 2020), 80--91.

Digital Library

[23]

Stefan Kögel. 2017. Recommender System for Model Driven Software Development. In 11th Joint Meeting on Foundations of Software Engineering (Paderborn, Germany) (ESEC/FSE 2017). Association for Computing Machinery, New York, NY, USA, 1026--1029.

Digital Library

[24]

John Krogstie, Odd Ivar Lindland, and Guttorm Sindre. 1995. Defining quality aspects for conceptual models. In ISCO.

[25]

Tobias Kuschke and Patrick Mäder. 2014. Pattern-Based Auto-Completion of UML Modeling Activities. In Proceedings of the 29th ACM/IEEE International Conference on Automated Software Engineering (Vasteras, Sweden) (ASE '14). Association for Computing Machinery, New York, NY, USA, 551--556.

Digital Library

[26]

Robert N Levine. 2008. A geography of time: On tempo, culture, and the pace of life. Basic Books. 288 pages.

[27]

O. I. Lindland, G. Sindre, and A. Solvberg. 1994. Understanding quality in conceptual modeling. IEEE Software 11, 2 (1994), 42--49.

Digital Library

[28]

Gunter Mussbacher, Daniel Amyot, Ruth Breu, Jean-Michel Bruel, Betty Cheng, and et al. 2014. The Relevance of Model-Driven Engineering Thirty Years from Now.

[29]

Gunter Mussbacher, Benoit Combemale, Jörg Kienzle, Silvia Abrahão, Hyacinth Ali, Nelly Bencomo, Márton Búr, Loli Burgueño, Gregor Engels, Pierre Jeanjean, Jean-Marc Jézéquel, Thomas Kühn, Sébastien Mosser, Houari Sahraoui, Eugene Syriani, Dániel Varró, and Martin Weyssow. 2020. Expert Voice: Opportunities in Intelligent Modeling Assistance. Software and Systems Modeling (2020).

[30]

Raja Parasuraman and Victor Riley. 1997. Humans and Automation: Use, Misuse, Disuse, Abuse. Human Factors 39, 2 (1997), 230--253. arXiv:https://doi.org/10.1518/001872097778543886

[31]

S. Pérez-Soler, G. Daniel, J. Cabot, E. Guerra, and J. de Lara. 2020. Towards automating the synthesis of chatbots for conversational model query. In Proc. of the Int. Conf. on Exploring Modeling Methods for Systems Analysis and Development. to appear.

[32]

Sara Pérez-Soler, Esther Guerra, and Juan de Lara. 2018. Collaborative Modeling and Group Decision Making Using Chatbots in Social Networks. IEEE Software 35, 6 (2018), 48--54.

[33]

A. Rocha, J. P. Papa, and L. A. A. Meira. 2010. How Far You Can Get Using Machine Learning Black-Boxes. In 2010 23rd SIBGRAPI Conference on Graphics, Patterns and Images. 193--200.

Digital Library

[34]

Michael Rovatsos and Gerhard Weiss. [n.d.]. Autonomous Software. 63--84.

[35]

Sagar Sen, Benoit Baudry, and Hans Vangheluwe. 2010. Towards Domain-specific Model Editors with Automatic Model Completion. Simulation 86, 2 (2010), 109--126.

Digital Library

[36]

Michael Spörk, Carlo Alberto Boano, and Kay Römer. 2020. Improving the Timeliness of Bluetooth Low Energy in Dynamic RF Environments. ACM Trans. Internet Things 1, 2, Article 8 (April 2020), 32 pages.

Digital Library

[37]

Kalaivani Subramaniam, Dong Liu, Behrouz Homayoun Far, and Armin Eberlein. 2004. UCDA: Use Case Driven Development Assistant Tool for Class Model Generation. In SEKE.

[38]

Ingo J. Timm, Peter Knirsch, Hans-Jörg Kreowski, and Andreas Timm-Giel. 2007. Autonomy in Software Systems. Springer Berlin Heidelberg, Berlin, Heidelberg, 255--273.

[39]

Jon Whittle, John Hutchinson, and Mark Rouncefield. 2014. The State of Practice in Model-Driven Engineering. Software, IEEE 31, 3 (2014), 79--85.

[40]

Yanlong Zhang, Hong Zhu, and S. Greenwood. 2005. Empirical validation of Website timeliness measures. In 29th Annual International Computer Software and Applications Conference (COMPSAC'05), Vol. 1. IEEE, 313 -- 318 Vol. 2.

Digital Library

Cited By

Pfeiffer JFuchß DKühn TLiebhart RNeumann DNeimöck CSeiler CKoziolek AWortmann AEgyed AWimmer MChechik MCombemale B(2024)Modeling Languages for Automotive Digital Twins: A Survey Among the German Automotive IndustryProceedings of the ACM/IEEE 27th International Conference on Model Driven Engineering Languages and Systems10.1145/3640310.3674100(92-103)Online publication date: 22-Sep-2024
https://dl.acm.org/doi/10.1145/3640310.3674100
Oakes BFamelis MSahraoui H(2023)Building Domain-Specific Machine Learning Workflows: A Conceptual Framework for the State of the PracticeACM Transactions on Software Engineering and Methodology10.1145/363824333:4(1-50)Online publication date: 21-Dec-2023
https://dl.acm.org/doi/10.1145/3638243
Mora Segura Áde Lara JWimmer M(2023)Modelling assistants based on information reuse: a user evaluation for language engineeringSoftware and Systems Modeling10.1007/s10270-023-01094-523:1(57-84)Online publication date: 17-Apr-2023
https://doi.org/10.1007/s10270-023-01094-5
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Index Terms

Towards an assessment grid for intelligent modeling assistance
1. General and reference
  1. Cross-computing tools and techniques
    1. Evaluation
2. Software and its engineering
  1. Software notations and tools
    1. Development frameworks and environments
      1. Application specific development environments
      2. Integrated and visual development environments
  2. Software organization and properties
    1. Software system structures
      1. Abstraction, modeling and modularity
      2. Software system models
        Model-driven software engineering

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cover image ACM Conferences

MODELS '20: Proceedings of the 23rd ACM/IEEE International Conference on Model Driven Engineering Languages and Systems: Companion Proceedings

October 2020

713 pages

ISBN:9781450381352

DOI:10.1145/3417990

Conference Chairs:
Esther Guerra
Universidad Autonoma de Madrid, Spain
,
Ludovico Iovino
Gran Sasso Science Institute, Italy

Copyright © 2020 ACM.

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Published: 26 October 2020

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MODELS '20: ACM/IEEE 23rd International Conference on Model Driven Engineering Languages and Systems

October 16 - 23, 2020

Virtual Event, Canada

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Cited By

Pfeiffer JFuchß DKühn TLiebhart RNeumann DNeimöck CSeiler CKoziolek AWortmann AEgyed AWimmer MChechik MCombemale B(2024)Modeling Languages for Automotive Digital Twins: A Survey Among the German Automotive IndustryProceedings of the ACM/IEEE 27th International Conference on Model Driven Engineering Languages and Systems10.1145/3640310.3674100(92-103)Online publication date: 22-Sep-2024
https://dl.acm.org/doi/10.1145/3640310.3674100
Oakes BFamelis MSahraoui H(2023)Building Domain-Specific Machine Learning Workflows: A Conceptual Framework for the State of the PracticeACM Transactions on Software Engineering and Methodology10.1145/363824333:4(1-50)Online publication date: 21-Dec-2023
https://dl.acm.org/doi/10.1145/3638243
Mora Segura Áde Lara JWimmer M(2023)Modelling assistants based on information reuse: a user evaluation for language engineeringSoftware and Systems Modeling10.1007/s10270-023-01094-523:1(57-84)Online publication date: 17-Apr-2023
https://doi.org/10.1007/s10270-023-01094-5
Adhikari BRapos EStephan M(2023)SimIMA: a virtual Simulink intelligent modeling assistantSoftware and Systems Modeling10.1007/s10270-023-01093-623:1(29-56)Online publication date: 13-Mar-2023
https://doi.org/10.1007/s10270-023-01093-6
Bergelin JStrandberg PKühn TSousa V(2022)Industrial requirements for supporting AI-enhanced model-driven engineeringProceedings of the 25th International Conference on Model Driven Engineering Languages and Systems: Companion Proceedings10.1145/3550356.3561609(375-379)Online publication date: 23-Oct-2022
https://dl.acm.org/doi/10.1145/3550356.3561609
Di Rocco JDi Ruscio DDi Sipio CNguyen PPierantonio A(2022)MemoRec: a recommender system for assisting modelers in specifying metamodelsSoftware and Systems Modeling10.1007/s10270-022-00994-222:1(203-223)Online publication date: 29-Mar-2022
https://doi.org/10.1007/s10270-022-00994-2
Sunkle SSaxena KPatil AKulkarni V(2022)AI-driven streamlined modeling: experiences and lessons learned from multiple domainsSoftware and Systems Modeling10.1007/s10270-022-00982-621:3(1-23)Online publication date: 19-Feb-2022
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Weyssow MSahraoui HSyriani E(2022)Recommending metamodel concepts during modeling activities with pre-trained language modelsSoftware and Systems Modeling10.1007/s10270-022-00975-521:3(1071-1089)Online publication date: 12-Feb-2022
https://doi.org/10.1007/s10270-022-00975-5
Almonte LGuerra ECantador Ide Lara J(2021)Recommender systems in model-driven engineeringSoftware and Systems Modeling10.1007/s10270-021-00905-xOnline publication date: 26-Jul-2021
https://doi.org/10.1007/s10270-021-00905-x

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