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Artificial intelligence empowered domain modelling bot

Published: 26 October 2020 Publication History

Abstract

With the increasing adoption of Model-Based Software Engineering (MBSE) to handle the complexity of modern software systems in industry and inclusion of modelling topics in academic curricula, it is no longer a question of whether to use MBSE but how to use it. Acquiring modelling skills to properly build and use models with the help of modelling formalisms are non-trivial learning objectives, which novice modellers struggle to achieve for several reasons. For example, it is difficult for novice modellers to learn to use their abstraction abilities. Also, due to high student-teacher ratios in a typical classroom setting, novice modellers may not receive personalized and timely feedback on their modelling decisions. These issues hinder the novice modellers in improving their modelling skills. Furthermore, a lack of modelling skills among modellers inhibits the adoption and practice of modelling in industry. Therefore, an automated and intelligent solution is required to help modellers and other practitioners in improving their modelling skills. This doctoral research builds an automated and intelligent solution for one modelling formalism - domain models, in an avatar of a domain modelling bot. The bot automatically extracts domain models from problem descriptions written in natural language and generates intelligent recommendations, particularly for teaching modelling literacy to novice modellers. For this domain modelling bot, we leverage the capabilities of various Artificial Intelligence techniques such as Natural Language Processing and Machine Learning.

References

[1]
Rick Adcock, Edward Alef, Bruce Amato, Mark Ardis, Larry Bernstein, Barry Boehm, Pierre Bourque, John Brackett, Murray Cantor, Lillian Cassel, et al. 2009. Curriculum guidelines for graduate degree programs in software engineering. (2009).
[2]
Chetan Arora, Mehrdad Sabetzadeh, Lionel Briand, and Frank Zimmer. 2016. Extracting domain models from natural-language requirements: approach and industrial evaluation. In MODELS 2016. ACM, 250--260.
[3]
Joanne M Atlee, TC Lethbridge, A Sobel, JB Thompson, and RJ LeBlanc. 2005. Software engineering 2004: ACM/IEEE-CS guidelines for undergraduate programs in software engineering. In Proceedings. 27th International Conference on Software Engineering, ICSE 2005. IEEE, 623--624.
[4]
Nelly Bencomo and Luis H Garcia Paucar. 2019. RaM: Causally-Connected and Requirements-Aware Runtime Models using Bayesian Learning. In MODELS 2019. IEEE, 216--226.
[5]
Jürgen Börstler, Ludwik Kuzniarz, Carl Alphonce, William B Sanders, and Michal Smialek. 2012. Teaching software modeling in computing curricula. ACM.
[6]
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.
[7]
Thaciana GO Cerqueira, Franklin Ramalho, and Leandro Balby Marinho. 2016. A Content-Based Approach for Recommending UML Sequence Diagrams. In SEKE. 644--649.
[8]
Akil Elkamel, Mariem Gzara, and Hanene Ben-Abdallah. 2016. An UML class recommender system for software design. In 2016 IEEE/ACS 13th International Conference of Computer Systems and Applications (AICCSA). IEEE, 1--8.
[9]
Ashok Goel, Brian Creeden, Mithun Kumble, Shanu Salunke, Abhinaya Shetty, and Bryan Wiltgen. 2015. Using watson for enhancing human-computer co-creativity. In 2015 AAAI Fall Symposium Series.
[10]
John Hutchinson, Jon Whittle, and Mark Rouncefield. 2014. Model-driven engineering practices in industry: Social, organizational and managerial factors that lead to success or failure. Science of Computer Programming 89 (2014), 144--161.
[11]
Mohd Ibrahim and Rodina Ahmad. 2010. Class diagram extraction from textual requirements using natural language processing (NLP) techniques. In 2010 Second International Conference on Computer Research and Development. IEEE, 200--204.
[12]
MS Jyothilakshmi and Philip Samuel. 2012. Domain ontology based class diagram generation from functional requirements. In 2012 12th International Conference on Intelligent Systems Design and Applications (ISDA). IEEE, 380--385.
[13]
Mathias Landhäußer, Sven J Körner, and Walter F Tichy. 2014. From requirements to UML models and back: how automatic processing of text can support requirements engineering. Software Quality Journal (2014).
[14]
Daniel Lucrédio, Renata P de M Fortes, and Jon Whittle. 2012. Moogle: a metamodel-based model search engine. Software & Systems Modeling 11, 2 (2012), 183--208.
[15]
William E McUmber and Betty HC Cheng. 2001. A general framework for formalizing UML with formal languages. In Proceedings of the 23rd International Conference on Software Engineering. ICSE 2001. IEEE, 433--442.
[16]
Azucena Montes, Hasdai Pacheco, Hugo Estrada, and Oscar Pastor. 2008. Conceptual model generation from requirements model: A natural language processing approach. In International Conference on Application of Natural Language to Information Systems. Springer, 325--326.
[17]
Hausi A Müller, Kenny Wong, and Scott R Tilley. 1995. Understanding software systems using reverse engineering technology. In Object-Oriented Technology for Database and Software Systems. World Scientific, 240--252.
[18]
Sara Pérez-Soler, Esther Guerra, Juan de Lara, and Francisco Jurado. 2017. The rise of the (modelling) bots: Towards assisted modelling via social networks. In ASE 2017. IEEE Press, 723--728.
[19]
S. Pérez-Soler, E. Guerra, and J. de Lara. 2018. Collaborative Modeling and Group Decision Making Using Chatbots in Social Networks. IEEE Software 35, 6 (November 2018), 48--54.
[20]
Gianna Reggio, Maurizio Leotta, Filippo Ricca, and Diego Clerissi. 2013. What are the used UML diagrams? A Preliminary Survey. EESSMOD@ MoDELS 1078, 10 (2013).
[21]
Iris Reinhartz-Berger. 2010. Towards automatization of domain modeling. Data & Knowledge Engineering 69, 5 (2010), 491--515.
[22]
Marcel Robeer, Garm Lucassen, Jan Martijn EM van der Werf, Fabiano Dalpiaz, and Sjaak Brinkkemper. 2016. Automated extraction of conceptual models from user stories via NLP. In RE 2016. IEEE, 196--205.
[23]
R. Saini, G. Mussbacher, J. L. C. Guo, and J. Kienzle. [n.d.]. DoMoBOT: A Bot for Automated and Interactive Domain Modelling. In 2nd Workshop on Artificial Intelligence and Model-driven Engineering (MDE Intelligence) (to be published).
[24]
R. Saini, G. Mussbacher, J. L. C. Guo, and J. Kienzle. [n.d.]. A Neural Network Based Approach to Domain Modelling Relationships and Patterns Recognition. In 10th International Model-Driven Requirements Engineering Workshop (MoDRE 2020) (to be published).
[25]
R. Saini, G. Mussbacher, J. L. C. Guo, and J. Kienzle. [n.d.]. Towards Queryable and Traceable Domain Models. In RE@Next! Track, 2020 IEEE 28th International Requirements Engineering Conference (RE) (to be published).
[26]
R. Saini, G. Mussbacher, J. L. C. Guo, and J. Kienzle. 2019. Teaching Modelling Literacy: An Artificial Intelligence Approach. In MODELS 2019 Companion. 714--719.
[27]
Omer Salih Dawood and Abd-El-Kader Sahraoui. 2017. From Requirements Engineering to UML using Natural Language Processing - Survey Study. European Journal of Industrial Engineering 2 (2017), 44--50.
[28]
Maxime Savary-Leblanc. 2019. Improving MBSE Tools UX with AI-Empowered Software Assistants. In 2019 ACM/IEEE 22nd International Conference on Model Driven Engineering Languages and Systems Companion (MODELS-C). IEEE, 648--652.
[29]
Rens Scheepers, Mary C Lacity, and Leslie P Willcocks. 2018. Cognitive Automation as Part of Deakin University's Digital Strategy. MIS Quarterly Executive 17, 2 (2018).
[30]
Ángel Mora Segura, Ana Pescador, Juan de Lara, and Manuel Wimmer. 2016. An extensible meta-modelling assistant. In 2016 IEEE 20th International Enterprise Distributed Object Computing Conference (EDOC). IEEE, 1--10.
[31]
Richa Sharma, Pratyoush K Srivastava, and Kanad K Biswas. 2015. From natural language requirements to UML class diagrams. In 2015 IEEE Second International Workshop on Artificial Intelligence for Requirements Engineering (AIRE). IEEE, 1--8.

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  • (2024)AI Assisted Domain Modeling Explainability and TraceabilityProceedings of the ACM/IEEE 27th International Conference on Model Driven Engineering Languages and Systems10.1145/3652620.3688197(130-135)Online publication date: 22-Sep-2024
  • (2024)Bibliometric Analysis of Model-Based Systems Engineering: Past, Current, and FutureIEEE Transactions on Engineering Management10.1109/TEM.2022.318663771(2475-2492)Online publication date: 2024
  • (2023)Machine Learning for Managing Modeling Ecosystems: Techniques, Applications, and a Research VisionSoftware Ecosystems10.1007/978-3-031-36060-2_10(249-279)Online publication date: 26-May-2023
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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
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 26 October 2020

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Author Tags

  1. artificial intelligence (AI)
  2. bot
  3. domain model
  4. machine learning (ML)
  5. natural language (NL)
  6. natural language processing (NLP)

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Overall Acceptance Rate 144 of 506 submissions, 28%

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Cited By

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  • (2024)AI Assisted Domain Modeling Explainability and TraceabilityProceedings of the ACM/IEEE 27th International Conference on Model Driven Engineering Languages and Systems10.1145/3652620.3688197(130-135)Online publication date: 22-Sep-2024
  • (2024)Bibliometric Analysis of Model-Based Systems Engineering: Past, Current, and FutureIEEE Transactions on Engineering Management10.1109/TEM.2022.318663771(2475-2492)Online publication date: 2024
  • (2023)Machine Learning for Managing Modeling Ecosystems: Techniques, Applications, and a Research VisionSoftware Ecosystems10.1007/978-3-031-36060-2_10(249-279)Online publication date: 26-May-2023
  • (2022)Bots in software engineering: a systematic mapping studyPeerJ Computer Science10.7717/peerj-cs.8668(e866)Online publication date: 9-Feb-2022
  • (2022)Creation, evaluation, and optimization of a domain-based dictionaryJournal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology10.3233/JIFS-22011043:5(6123-6136)Online publication date: 1-Jan-2022
  • (2021)Automated Traceability for Domain Modelling Decisions Empowered by Artificial Intelligence2021 IEEE 29th International Requirements Engineering Conference (RE)10.1109/RE51729.2021.00023(173-184)Online publication date: Sep-2021
  • (2021)Software Design and Artificial Intelligence: A Systematic Mapping Study2021 9th International Conference in Software Engineering Research and Innovation (CONISOFT)10.1109/CONISOFT52520.2021.00028(132-141)Online publication date: Oct-2021

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