1. Mining Metamodels From Instance Models: The MARS System Faizan Javed Department of Computer & Information Sciences, University of Alabama at Birmingham The Fourth Annual Southeastern Software Engineering Conference (SE) 2 30 th March 2005

2. Motivation  Software artifacts like models and source code conform to a schema, and are stored in a repository.  Evolution of schema might be required to address new feature requests.  Repository artifacts might become obsolete if not transformed to conform to new schema.  Example: Java SDK – new versions restructure or rename API’s.

3. Domain-Specific Modeling (DSM)  Raises level of abstraction, while narrowing design space to single domain of discourse.  Allows construction of models which follow domain abstractions and semantics allowing developers to work with domain concepts.  Metamodel: Defines key elements of domain.  Models: Defines specific configurations of the domain.

4. DSM modeling with GME  GME: Generic Modeling Environment  Metamodel: Networking

5. DSM Modeling with GME  Model: CompanyA

6. Challenges of Mining Domain Instance Models  Metamodel drift: inability to load models due to changes to metamodel.  Solution: Infer metamodel from instance models !  Grammar Inference community has done extensive work on similar problem, albeit for programming language domain.

7. Challenges of Mining Domain Instance Models  Idea: Apply grammar inference techniques to the metamodel inference problem.  Problem: Modeling tools export XML files; mismatch in representation expected by grammar inference techniques.  Solution: Translate XML to textual DSL (Domain-Specific Language) !

8. Tools Used in the Project  GME: Metamodel described with UML class diagrams, and constraints with OCL.  LISA: An interactive environment where users can specify, generate, compile, and execute programs in a newly specified language.  DMS: A program transformation system and re-engineering toolkit.

9. Overview of the MARS system

10. From GME models to MRL (Model Representation Language) ……. model NetDiagram { WSGroup ; Perimeter ; Host ; Network ; WSGroup ; Host ; Router ; fields; connections Connection : Port -> Network ; Connection : Host -> Network ; Connection : Port -> Perimeter ; Connection : WSGroup -> Network ; Connection : Host -> Network ; } ……….. XSLT

11. From MRL to Inferred Metamodel ……. model NetDiagram { WSGroup ; Perimeter ; Host ; Network ; WSGroup ; Host ; Router ; fields; connections Connection : Port -> Network ; Connection : Host -> Network ; Connection : Port -> Perimeter ; Connection : WSGroup -> Network ; Connection : Host -> Network ; } ……….. LISA+DMS

12. Inferred vs. Original Metamodel

13. Limitations and Observations  Generalization hierarchy can’t be inferred resulting in more elements in inferred metamodel.  Domain-Specific Visualization: graphic assigned to metamodel entities cant be inferred.  OCL Constraints: capture domain semantics that cant be captured with static diagrams. They are not explicitly indicated in domain models.

14. Related Work  The XTRACT System: 1) Infers DTD from XML documents 2) Derives a regular expression for each element in the XML document. 3) Uses the Minimum Description Length (MDL) principle to choose the best DTD from a set of candidate DTDs o ECFG Based System (Chidlovskii): 1) Represents XML documents as structured examples of an unknown ECFG. 2) Uses existing grammar inference techniques to infer the ECFG.

15. For more information:  Project Website:  http://www.cis.uab.edu/softcom/GenParse/mars.htm