Future Trends on Software and Systems Modeling
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Jordi Cabot discusses future trends in modeling, emphasizing the significance of abstraction in software engineering and the evolving role of modeling in various domains, particularly in the context of low-code development and machine learning. He outlines challenges such as usability, infrastructure, and perceptions of modeling in the industry, advocating for more flexible, user-friendly approaches and the integration of AI to enhance productivity. The document presents various innovative techniques and frameworks for modeling, including the use of textual models and cloud-based solutions, while stressing the need for domain-specific adaptations.
Future Trends on Software and Systems Modeling
- 1. Future Trends on Modeling 18th of June, 2020 Jordi Cabot ICREA Research Professor at UOC [email protected] @softmodeling / modeling-languages.com #SiriusCon
- 3. I model, therefore I am - René Descartes
- 4. • Grady Booch – history of softwre engineering The entire history of software engineering is that of the rise in levels of abstraction - Grady Booch
- 6. To model, or not to model, this is the WRONG question - Shakespeare
- 7. What/when/how many models ? Depends on: Size, Team, Domain,…. Real question (difficult!) Modeling ROI: Cost of modeling vs Bº of modeling <- Cost depends on tools!
- 9. This is a model (that you all understand)
- 10. Engineering Systems SW Everything is a model SW is probably the worst market for selling modeling
- 11. SysMLe Profile: a SysML Profile for ECAD
- 12. Modeling in…
- 13. Moving towards modeling is the natural evolution in any domain (e.g. Machine Learning)
- 14. Why not more modeling?
- 15. BUT he is saying this since 1985
- 16. History of ML History of Modeling Langs – J.P. Tolvanen
- 17. We have a marketing problem
- 18. Modeling is not sexy (in some communities, e.g. devs) • Low-code is modeling with another name that just sells more • Modeling has bad press (mostly due to salesmen selling UML as a silver bullet) • Exception: Reverse Engineering (OpenAPItoUML, JSONDiscoverer,…) People believe that modeling … • Is not agile • Is just for documentation (and a posteriori) • Is just nice pictures
- 19. VCs believe in “modeling” (when marketing it well!)
- 20. We have a usability problem
- 21. Have you ever found a user that loves her modeling tool? • Too many clicks • Too many options • Eclipse/EMF was a great environment Not a Good first impression • Lack of documentation • Installation/Configuration issues • Generated code not optimized
- 22. We have an infrastructure problem
- 23. Modeling is also: • Collaboration • Security • Confidentiality Adapted to where the users are/want • GitHub (textual modeling) • Online (web-based editors) • Flexible (inconsitent, lightweight MDE)
- 24. And there’s more
- 25. Bucchiarone, Cabot, Paige, Pierantonio: Grand challenges in model-driven engineering: an analysis of the state of the research. Software and Systems Modeling 19(1): 5-13 (2020)
- 26. What about better modeling?
- 27. New Interfaces for Modeling
- 28. Textual models are models • Lower barrier to entry • Easier to integrate in CI pipelines • One DSL -> multiple notations – Killer combination: textual notation to write the model, graphical one to visualize & read it
- 29. E.g. Modeling of chatbots (xatkit.com) Daniel, Cabot, Deruelle, Derras: Xatkit: A Multimodal Low-Code Chatbot Development Framework. IEEE Access 8: 15332-15346 (2020)
- 30. Moving to the cloud • All Programming IDEs are moving to the cloud • Modeling IDEs should follow suit • Multiple JS libraries can be used in the front-end • In the back-end: EMF-REST
- 31. Modeling outside modeling tools
- 32. Modeling with VoiceBots / Chatbots Pérez-Soler, Daniel, Cabot, Guerra, de Lara: Towards Automating the Synthesis of Chatbots for Conversational Model Query. EMMSAD@CAiSE 2020: 257-265
- 33. Openness comes with new challenges • Intellectual Property Protection – Robust Hashing for models • Accountability – Blockchain infrastructure for models • Security – Access-control for models • Scalability – NoSQL backends like NeoEMF
- 35. Smarter tools makes us more productive https://modelia.eu/
- 36. Mendix Assist
- 37. OutSystems.ai
- 38. Model autocompletion from textual data • Previous approaches are based on historical models. Only useful if you have many models • What every project has is lots of documents • We’re developing an NLP-based model autocompletion
- 39. AI for manipulationg models: the MT example Original model … CODE Software code1 refinementst n refinementth Model-to-model Transformation Model-to-text Transformation CODE CODE • Requires learning a new language (the MT Language) • Time consuming • Error prone
- 40. Let’s try to learn the MTs automatically Input Output Training Transforming ML Input OutputML Machine Learning Artificial Neural Networks Deep Networks Recurrent networks LSTM BPMN Petri nets BPMN Petri Net
- 41. Tree-to-Tree Encoder-decoder arquitecture InputModel (preprocessed) InputTree EmbeddingLayer Encoder LSTM network OutputTree ExtractionLayer OutputModel (non-postprocessed) Decoder LSTM network AttentionLayer InputModel OutputModel Preprocessing Postprocessing Burgueño, Cabot, Gérard: An LSTM-Based Neural Network Architecture for Model Transformations. MoDELS 2019: 294-299 We are developed a similar approach for model-to-code transformations. Useful to mimick the company’s programming style.
- 43. We live in a modeling multi-verse We need to develop domain- specific modeling environments to better serve the needs of users in different domains
- 44. Models are not a static, fixed and complete artifact. Rather a partial, dynamic, personal and temporal view of the domain How tools and languages should adapt to better serve this view?
- 45. Customization • It should be easy to adapt tools to your specific needs – Even better: morphing modeling tools that automatically adapt based on what you do. • Also the notations – Personal notations sharing a common abstract syntax
- 46. Views and viewpoints Brunelière, García Perez, Wimmer, Cabot: EMF Views: A View Mechanism for Integrating Heterogeneous Models. ER 2015: 317-325 EMF Views
- 47. Extensible/Composable DSLs • No one size fits all solution. Let’s not reinvent UML • Set of modeling libraries/packages to import into a small core DSL – Uncertainty modeling – Temporal modeling – Spatial modeling • Somehow going back to the idea of (EMF) Profiles
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