Model Management in Model-Driven Engineering
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This document provides an overview of model management techniques investigated by the author, including coupled evolution, semantic issues in bidirectional model transformations, and the MDE Forge collaborative modeling platform. It introduces model-driven engineering (MDE) and discusses how models provide abstraction and can be automated through model transformations to perform complex tasks like incremental changes and traceability management. The challenges of metamodel and model co-evolution are described, as well as approaches to manage changes across an entire MDE ecosystem. Uncertainty in bidirectional model transformations is also covered.
Model Management in Model-Driven Engineering
- 1. Dipartimento di Ingegneria e Scienze Università degli Studi dell’Aquila dell’Informazione e Matematica Model Management in Model- Driven Engineering Alfonso Pierantonio
- 2. 2 University of Alberta - May 30, 2016 Introduction In this presentation, I’ll overview different model management we have been investigating over the last years – Coupled Evolution – Semantic Issues in Bidirectional Model Transformations – MDE Forge Collaborative Modeling Platform
- 3. A brief introduction to Model-Driven Engineering
- 4. 4 University of Alberta - May 30, 2016 Model-Driven Engineering A software discipline that shifts the focus of software development from coding to modeling Models – are abstractions representing knowledge and activities that govern a particular application domain – use domain concepts rather than computing concepts, ie they can be defined/used by non computer scientists – have first-class status
- 5. Model-Driven Engineering = Abstraction + Automation
- 6. 6 University of Alberta - May 30, 2016 Abstraction Models are abstractions that enable to deal with complex reality in a simplified way – early analysis to detect faults or unwanted behavior – communication between stakeholders, designers, etc – to persist and retain knowledge within an organization Models have a purpose: they can be used in a cost- effective way (simpler, safer, cheaper) in place of reality for some cognitive purpose.
- 9. wrong models lead to wrong conclusions
- 10. University of Alberta - May 30, 2016 10 Wrong models: four humours Hippocrates believed the body contained four fluids, called humours: blood, yellow bile, black bile, and phlegm. Bloodletting was used to remove excess humour to restore balance and cure sickness. Physician Benjamin Rush (one of the signers of the Declaration of Independence) recommended bloodletting to George Washington, the first U.S. president, who died after having 3.75 liters of blood removed from his body within a 10 hour period as treatment for a throat infection.
- 12. Model-Driven Engineering = Abstraction + Automation
- 13. 13 University of Alberta - May 30, 2016 Automation A model transformation is a program that mutates one model into another – production of concrete models from abstract ones (and viceversa) – keeping two or more models synchronized or in a consistent state – models must be formal and processable m1 m2 m3 m4 viewpoint v1 viewpoint v2
- 14. 14 University of Alberta - May 30, 2016 Transformations Model transformations are required to perform complex tasks, eg – non destructive incremental changes – round-tripping engineering, – traceability management Using standard programming languages is challenging, suboptimal, and prone to errors
- 15. 15 University of Alberta - May 30, 2016 Summary – Introduction – Coupled Evolution – Uncertainty in Bidirectional Model Transformations – MDE Forge Collaborative Modeling Platform
- 16. 16 University of Alberta - May 30, 2016 Metamodeling Architecture The four-layer meta architecture is present in many technical spaces.
- 17. University of Alberta - May 30, 2016 17 Modeling Ecosystems Metamodels formalize domains and are cornerstones of modeling ecosystems – A wide range of artifacts are defined upon metamodels – Changing a metamodel might invalidate many components of the ecosystem
- 19. A Petri Net Model Petri Net Metamodel Conformance
- 20. A Petri Net Model GMF Editor Petri Net Metamodel GMF Editors
- 21. A Petri Net Model GMF Editor Petri Net Metamodel Transformation Transformations
- 22. A Petri Net Model GMF Editor Petri Net Metamodel Transformation Code Generators
- 23. A Petri Net Model GMF Editor Petri Net Metamodel Transformation
- 24. A simple metamodel evolution
- 25. 25 University of Alberta - May 30, 2016 A simple metamodel evolution Let us consider a simple refactoring of a (simple) Petri Net metamodel – A metaclass renaming – Reference merge – New Metaclasses added
- 26. 26 University of Alberta - May 30, 2016 Petri Net revised Initial Version
- 27. 27 University of Alberta - May 30, 2016 Petri Net revised Initial Version conformsTo
- 28. 28 University of Alberta - May 30, 2016 Petri Net revised Initial Version Final Version conformsTo
- 29. 29 University of Alberta - May 30, 2016 Petri Nets revised Initial Version Final Version Metaclass renaming Reference merge New Metaclasses
- 30. 30 Petri Nets revised Despite the simplicity, such changes are enough for invalidating most of artifacts in the ecosystem Initial Version Final Version Metaclass renaming Reference merge New Metaclasses
- 31. 31 University of Alberta - May 30, 2016 Metamodel/Model co-evolution – The model is not conforming to the Petri Net metamodel anymore – Adaptations are needed to restore conformance
- 32. 32 University of Alberta - May 30, 2016 Metamodel/Model co-evolution
- 33. 33 University of Alberta - May 30, 2016 Metamodel changes The changes can be classified according to their effect on the models – non-breaking changes do not break the conformance – breaking and resolvable changes break the conformance of models, although they can be automatically co-adapted – breaking and unresolvable changes break the conformance which can not be automatically restored The classification is artifact specific, eg. what holds for models does not hold for transformations
- 35. 0..* Artifact Impact Models An optional metaclass does not compromise the conformance of existing models Transformations A new rule is needed to “consume” the new instances Editors A new widget in the palette must be added Code generators Templates must be extended … (new metaclass)
- 36. 36 University of Alberta - May 30, 2016 A Babel of Tools and Techniques Too many different techniques, difficulties in having a consistent and coherent adaptation of the ecosystem Metamodel COPE Flock Täntzer et al (ICGT’12) Levendovszky O. Diaz (SLE’12) GMF Evolution Focus Model Model Model Transformation Transformation Editor
- 37. 37 University of Alberta - May 30, 2016 Approaches There are different kinds of approaches – Inductive: migration strategies are directly derived from metamodel differences – Programatic: migration procedure are specified by means of dedicated languages or libraries The main difficulty is due to the ambiguity of the migration process – Several alternatives are typically available – Difficulty in “capturing” the rationale behind migration to be reused across the different artifacts
- 38. 38 University of Alberta - May 30, 2016 EMF Migrate It is a declarative DSL devoted to the co-evolution management of any artifact in the ecosystem, it permits It is agnostic of the differencing technique Davide Di Ruscio, Jürgen Etzlstorfer, Ludovico Iovino, Alfonso Pierantonio and Wieland Schwinger. Supporting variability exploration and resolution during model migration. ECMFA 2016, STAF Wien (to appear) Di Ruscio, Davide, Ludovico Iovino, and Alfonso Pierantonio. "Coupled evolution in model-driven engineering." Software, IEEE 29.6 (2012): 78-84. Iovino, Ludovico, Alfonso Pierantonio, and Ivano Malavolta. "On the Impact Significance of Metamodel Evolution in MDE." Journal of Object Technology 11.3 (2012): 3-1. Di Ruscio, Davide, Ludovico Iovino, and Alfonso Pierantonio. "Evolutionary Togetherness: How to Manage Coupled Evolution in Metamodeling Ecosystems." ICGT. Vol. 7562. 2012. Di Ruscio, Davide, Ralf Lämmel, and Alfonso Pierantonio. "Automated co-evolution of GMF editor models." Software Language Engineering. Springer Berlin Heidelberg, 2010. 143-162. A. Cicchetti, D. D. Ruscio, R. Eramo, and A. Pierantonio, “Automating Co-evolution in Model-Driven Engineering,” Enterprise Distributed Object Computing Conference, 2008. EDOC '08. 12th International IEEE, pp. 222–231, 2008.
- 39. 39 University of Alberta - May 30, 2016 Summary – Introduction – Coupled Evolution – Uncertainty in Bidirectional Model Transformations – MDE Forge Collaborative Modeling Platform
- 40. 40 University of Alberta - May 30, 2016 Bidirectionality in Model Transformations Bidirectionality is about keeping a set or related models synchronized or in a consistent state: updates to a source entail updates to the others.
- 41. University of Alberta - May 30, 2016 41 Bidirectionality in Model Transformations Bidirectionality is about keeping a set or related models synchronized or in a consistent state: updates to a source entail updates to the others. 2
- 42. 42 University of Alberta - May 30, 2016 Uncertainty in modeling Uncertainty in modeling has been investigated in different areas – requirement engineering – partial modeling – change propagation – variability modeling – sets of models Uncertainty in model transformations makes their behavior non-deterministic and difficult to be kept under control P. Stevens, “Bidirectional model transformations in QVT: semantic issues and open questions,” Softw Syst Model, vol. 9, no. 1, pp. 7–20, Dec. 2008.
- 43. 43 University of Alberta - May 30, 2016 Uncertainty in modeling Uncertainty in modeling has been investigated in different areas – requirement engineering – partial modeling – change propagation – variability modeling – sets of models Uncertainty in model transformations makes their behavior non-deterministic and difficult to be kept under control P. Stevens, “Bidirectional model transformations in QVT: semantic issues and open questions,” Softw Syst Model, vol. 9, no. 1, pp. 7–20, Dec. 2008. No emerging language so far, QVT-R implementations are present severe semantic issues
- 44. in(card) startup Out of Service Off Idle Self Test Maintenance Active Authentication Selecting transaction Transactionfailed doneshutdown in(card)cancel error fixed Out of Service Off Idle Active shutdown cancel error fixed startup done 2 2
- 45. in(card) startup Out of Service Off Idle Self Test Maintenance Active Authentication Selecting transaction Transactionfailed doneshutdown in(card)cancel error fixed Out of Service Off Idle Active shutdown cancel error fixed startup done 2 2
- 46. in(card) startup Out of Service Off Idle Self Test Maintenance Active Authentication Selecting transaction Transactfailed doneshutdown in(card)cancel error fixed Out of Service Off Idle Active shutdown cancel error fixed startup done
- 47. in(card) startup Out of Service Off Idle Self Test Maintenance Active Authentication Selecting transaction Transactfailed doneshutdown in(card)cancel error fixed Out of Service Off Idle Active shutdown cancel error fixed startup done How this manual change can be back propagated?
- 48. in(card) startup Out of Service Off Idle Self Test Maintenance Active Authentication Selecting transaction Transactfailed doneshutdown in(card)cancel error fixed Out of Service Off Idle Active shutdown cancel error fixed startup done The update entails multiple choices
- 49. 49 University of Alberta - May 30, 2016 Why non-determinism/uncertainty? Consistency management can be seen as an update view problem. The transformation is not-deterministic because 1) Metamodels are not isomorphic 2) Multiple update strategies are possible, a general consistency-restoration strategy cannot always at design-time Unknown uncertainty: the designer does not hold enough information for deciding a priori what is the “wanted” solution Zan, Tao, Hugo Pacheco, and Zhenjiang Hu. "Writing bidirectional model transformations as intentional updates." Procs. 36th International Conference on Software Engineering, 2014. Bancilhon, François, and Nicolas Spyratos. "Update semantics of relational views." ACM Transactions on Database Systems (TODS) 6.4 (1981): 557-575.
- 50. 50 University of Alberta - May 30, 2016 Why non-determinism/uncertainty? Consistency management can be seen as an update view problem. The transformation is not-deterministic because 1) Metamodels are not isomorphic 2) Multiple update strategies are possible, a general consistency-restoration strategy cannot always at design-time Unknown uncertainty: the designer does not hold enough information for deciding a priori what is the “wanted” solution Zan, Tao, Hugo Pacheco, and Zhenjiang Hu. "Writing bidirectional model transformations as intentional updates." Procs. 36th International Conference on Software Engineering, 2014. Bancilhon, François, and Nicolas Spyratos. "Update semantics of relational views." ACM Transactions on Database Systems (TODS) 6.4 (1981): 557-575.Warning! Non-determinism is evident only at runtime
- 51. 51 University of Alberta - May 30, 2016 Janus Transformation Language The JTL language provide an explicit support to non- determinism: – programmers need only specify a consistency relation, allowing the bx engine to resolve the under-specification non- deterministically” – From unknown to known uncertainty! – A categorical characterization of JTL Alessio Bucaioni, Antonio Cicchetti, Federico Ciccozzi, Saad Mubeen, Alfonso Pierantonio, Mikael Sjödin. “Handling Uncertainty in Automatically Generated Implementation Models in the Automotive Domain”, Proceedings SEEA, 2016 (to appear) Eramo, Romina, Alfonso Pierantonio, and Gianni Rosa. "Managing uncertainty in bidirectional model transformations." Proceedings of the 2015 ACM SIGPLAN International Conference on Software Language Engineering. ACM, 2015. Best paper award. Eramo, Romina, Alfonso Pierantonio, and Gianni Rosa. "Uncertainty in bidirectional transformations." Proceedings of the 6th International Workshop on Modeling in Software Engineering. ACM, 2014. Cicchetti, Antonio, et al. "JTL: a bidirectional and change propagating transformation language." Software Language Engineering. Springer Berlin Heidelberg, 2010. 183-202. Zinovy Diskin, Romina Eramo, Alfonso Pierantonio, Krzysztof Czarnecki: “Incorporating Uncertainty into Bidirectional Model Transformations and their Delta-Lens Formalization.” Procs. 5th Intl Workshop on Bidirectional Transformations, ETAPS 2016: 15-31
- 52. 52 University of Alberta - May 30, 2016 Current Research Directions Main research interests in Model Driven Engineering – Coupled Evolution – Uncertainty in Bidirectional Model Transformations – MDE Forge Collaborative Modeling Platform
- 53. 53 University of Alberta - May 30, 2016 MDE Forge Complex model management as-a-service – most functionalities are restful Collaborative modeling platform – workspace/project/team management – artifact sharing mechanism Open architecture to accommodate third-party functionalities – core components – extensions
- 54. Feature soon (already implemented) later (Q2 2017) maybe formalized as a megamodel ✓ document persistency ✓ free user registration ✓ development team support ✓ workspace templating ✓ search by-example ✓ advanced query language ✓ advanced browsing/clustering ✓ full EMF support ✓ ATL transformation engine ✓ ATL static validation & testing ✓ ETL transformation engine ✓ automated transformation chaining ✓ metrics extraction ✓ user-defined quality models ✓ collaborative diagrammatic editor ✓ …
- 55. 55 University of Alberta - May 30, 2016 Core Service
- 56. 56 University of Alberta - May 30, 2016 ATL Extension
- 57. 57 University of Alberta - May 30, 2016 Metamodel clustering tecniques
- 58. 58 University of Alberta - May 30, 2016 Digging the repository
- 59. 59 University of Alberta - May 30, 2016 Digging the repository Repository wide query language for mining the complete set of available artifacts mdeforge> mdeforge> foreach (mm in mdeforge.getMetamodels()) { List<String> results; EClass.allInstances(mm).foreach(x -> results.add(x.getName())); EAttribute.allInstances(mm).foreach(x -> results.add(x.getName())); EReference.allInstances(mm).foreach(x -> results.add(x.getName())) } > 257,347 elements found in 2422 metamodels mdeforge>
- 60. 60 University of Alberta - May 30, 2016 MDE Forge http://www.mdeforge.org Example – Dashboard http://www.mdeforge.org/mdeforge/public/dashboard – Clustering http://www.mdeforge.org/public/browse/cluster_graph
- 61. Di Rocco, J., Di Ruscio, D., Iovino, L., & Pierantonio, A. (2015). Collaborative Repositories in Model-Driven Engineering. IEEE Software, (3), 28-34.
- 62. 62 University of Alberta - May 30, 2016 Conclusions Model-Driven Engineering is about related models cross- linked by automated operations and existing at – different level of abstractions, and – different stages Uncertainty is inherent to most of the operations that include – Model transformations – Multi-layer model transformations (coupled evolution) – Model comparison/differencing – Traceability management – Solution space exploration – etc
- 63. Thanks!
Editor's Notes
- #43: Uncertainty in RE is about uncertainty in general sw development, it is quite broad and often is about accuracy, clarity and inconsistencies Partial modeling is a way of dealing with forms of uncertainty, existing approaches started from behavioral modeling where forms of indeterminism were presented
- #44: Uncertainty in RE is about uncertainty in general sw development, it is quite broad and often is about accuracy, clarity and inconsistencies Partial modeling is a way of dealing with forms of uncertainty, existing approaches started from behavioral modeling where forms of indeterminism were presented