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Uploaded byJuan José Cadavid Gómez
Point GEODES
This document discusses assisting domain modeling with formal analysis. It summarizes that domain models describe entities and relationships in a system. Typical domain modeling results in an incorrect raw modeling space. Precise domain modeling uses constraints like OCL to limit solutions and find the precise modeling space. The document presents an example of feature model metamodeling and constraints. It describes challenges with the Pramana tool for exploring modeling spaces. Finally, it discusses understanding OCL usage patterns, automatically deriving invariants from patterns, and potential algorithms for mining patterns.
Point GEODES
- 1. Assisting Domain Modeling with Formal Analysis Juan Cadavid Benoit Baudry Jean-Marc Jézéquel INRIA Bretagne - Atlantique, team Triskell 1
- 2. Domain Models • A conceptual model of a system which describes the various entities involved in that system and their relationships. Domain Model for Insurance 2
- 3. Typical Domain Modeling Domain Model Raw Modeling Space 3
- 4. Typical Domain Modeling Incorrect Instances There is an intention not expressed by the metamodel on its own. 4
- 5. Precise Domain Modeling Limit the number of solutions to find the precise modeling space 5
- 6. Precise Domain Modeling OCL • Write constraints along with the domain model. OCL OCL • The most popular language is OCL OCL (Object Constraint OCL Language) 6
- 7. Example: Metamodeling Feature Models A B B 7
- 8. Example: Metamodeling Feature Models A B B Two features cannot have the same name 7
- 9. Example: Metamodeling Feature Models 8
- 10. Example: Metamodeling Feature Models There cannot be mutual exclusivity between a parent feature and its children. 8
- 11. Example: Metamodeling Feature Models A B 9
- 12. Example: Metamodeling Feature Models A B The or operator must have at least two operands 9
- 13. Precise Domain Modeling • A given set of OCL constraints could include bad instances, and leave OCL out good instances. OCL • How to find the good set of OCL OCL constraints that correctly describes the modeling space? 10
- 14. Exploring the Modeling Space:Generating Instances Kermeta2Alloy Metamodel Metamodel in Ecore in Alloy Pramana: Generates conforms to AlloySolution Metamodel Instances using Solution2xmi Instances Instances Alloy in XMI in Alloy XML 11
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- 16. Challenges with Pramana • Robustness/Enough testing • Alloy limitations • Small scope • Integer numbers • Maximize distance between metamodel instances • OCL2Alloy: A difficult transformation 13
- 17. Understanding the use of OCL (1) • Study of 519 invariants from three OMG specifications: UML, OCL and CORBA Component Model • Definition of Metrics • Size of Metamodel • Invariant complexity w.r.t. the metamodel • Invariant complexity w.r.t to OCL 14
- 18. Understanding the use of OCL (1I) • 88% of invariants were written with 36% of the OCL language. • Most invariants are defined over two or less domain elements (i.e. class attributes) • Result: Catalog of recurring patterns in OCL invariants. 15
- 19. Automatic Invariant Derivation (I) • Prototype to match patterns from Catalog • Input: Metamodel, correct instances and incorrect instances. • Matching instances marked as incorrect by the user • Incorrect motifs • Derive OCL invariant from template • Test derived instances marked as correct by the user 16
- 20. Automatic Invariant Derivation (II) Pattern 1 Pattern 2 Pattern n Ecore+OCL Ecore+OCL Ecore+OCL Metamodel Automatic Metamodel Correct Invariant + OCL Instances Derivation Incorrect Instances 17
- 21. Strategies for Mining patterns • Strings -> bio-inspired computing, bioinformatics • Graphs -> graph grammars, mining graph bases • Relational -> R, data mining algorithms
- 22. Algorithms • We need: • Match existing patterns (from a predefined catalog) • Find new patterns (mining correct/incorrect instances) • We could get them from: • Bio-inspired computing, bioinformatics • Graph grammars: structure learning, PEGG • Relational data-mining • Inductive-Logic programming
- 23. Protocol List<OclExpression> mineInvs(List<Object> goodInst, List<Object>badInst){ #Encode instances #Match with existing patterns from catalog #Find new patterns }