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«Aditya Agrawal Dissertation Submitted to the Faculty of the Graduate School of Vanderbilt University in partial fulfillment of the requirements for ...»

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A FORMAL GRAPH TRANSFORMATION BASED LANGUAGE FOR MODEL-TOMODEL TRANSFORMATIONS

By

Aditya Agrawal

Dissertation

Submitted to the Faculty of the

Graduate School of Vanderbilt University

in partial fulfillment of the requirements

for the degree of

DOCTOR OF PHILOSOPHY

in

Electrical Engineering

August, 2004

Nashville, Tennessee

Approved: Date:

________________________________________________ ___________________

________________________________________________ ___________________

________________________________________________ ___________________

________________________________________________ ___________________

________________________________________________ ___________________

________________________________________________ ___________________

To my source of Inspiration, My parents & To Pramila, the love of my life ii

ACKNOWLEDGEMENTS

The DARPA/IXO MOBIES program, Air Force Research Laboratory under agreement number F30602-00-1-0580 and NSF ITR on "Foundations of Hybrid and Embedded Software Systems" programs have supported, in part, the research described in this dissertation. Some tools described in this dissertation have been developed by other members of the MoBIES team. Feng Shi developed in part the Graph Rewriting Engine (GRE), Zsolt Kalmar developed the Graph Rewriting Debugger (GRD) and Attila Vizhanyo developed the Code Generator (CG).

To begin with I would like to thank Dr. Gabor Karsai my academic advisor and the chair of my dissertation committee. He has motivated and guided me through this endeavor. His knowledge and patience are virtues I can only dream of achieving. I am grateful to members of my dissertation committee, Dr. Janos Sztipanovits, Dr. Douglas Schmidt, Dr. Gautam Biswas, Dr. Jeremy Spinrad and Dr. Mark Ellingham for keeping my focus on the goals and for directing me back on track when I veered. The MoBIES team consisting of Dr. Gyula Simon, Dr. Sandeep Neema, Feng Shi, Attila Vizhanyo, Zsolt Kalmar, Andras Lang, Tamas Paka and Anantha Narayanan deserve my heartiest thanks for being the greatest teams to work with.

Last but definitely not the least I would like to thank mom and dad for believing in me all through the journey and encouraging me to push forward whenever I was tired.

Without the training I have received from them I would never have reached where I am.

The rest of my family, Jiten, Shilpa, Roma, Sandhir, Mona, Rashi and Sparsh has played a vital role in my endeavor. They have been by my side at every crossroad of life helping

–  –  –

ACKNOWLEDGEMENTS

TABLE OF CONTENTS

LIST OF TABLES

LIST OF FIGURES

LIST OF ABBREVIATIONS

Chapter I. INTRODUCTION

II. BACKGROUND

Model Based Software Engineering

Model Classification

Low-Level and High-Level Modeling Languages

High-level models

Low-Level Vs High-Level

Domain-Specific and Domain-Independent Languages

Domain Specific Vs Domain Independent

Textual and Graphical Languages

Generative and Model Based Solutions

Generative Programming (GP)

Model Integrated Computing (MIC)

Summary of Model-Based Solutions

Graph Grammars And Transformations

Node Replacement Graph Grammars

Node Label Controlled (NLC)

Neighborhood Controlled Embedding (NCE)

Hyperedge Replacement Graph Grammars

Algebraic Approach to Graph Transformation

Programmed Graph Rewriting Systems

Programmed Structure Replacement Systems

Summary of Graph Grammars and Transformations

Graph Transformation Based Tools

PROGRES

AGG

Comparison of Features

–  –  –

III. RESEARCH PROBLEM, HYPOTHESIS AND METHODS

Research Hypothesis

Research Methods

Completion Criteria

IV. GREAT: A MODEL-TO-MODEL TRANSFORMATION LANGUAGE.............. 71

Heterogeneous Graph Transformations

Definitions

The Pattern Specification Language

Simple Patterns

Fixed Cardinality Patterns

Extending the Set Semantics

Cardinality for Edges

Variable Cardinality

Pattern Graph and Match Definition

Graph Rewriting/Transformation Language

Language Realization

The Language For Controlled Graph Rewriting And Transformation.............. 90 Sequencing of Rules

Hierarchical Rules

Branching using test case

Non-deterministic Execution

Termination

Enabling Optimized Graph Transformations

Typed Patterns

Pivoted Pattern Matching

Reusing Previously Matched Objects

User Controlled Traversal

V. THE EXECUTION FRAMEWORK FOR GREAT

Concrete Syntax

Abstract

Syntax

Execution Engine

Graph Rewriting Debugger (GRD)

Code Generator

Comparison of CG with GRE

Integrated Development Environment

Model Development Tools

Execution Invocation Tools

VI. A CASE STUDY – SIMULINK/STATEFLOW TO HSIF





The Inputs and Outputs of the Semantic Translator

vi The output: HSIF

The input: A subset of the MSS language

Example: Tank Level Control

Implementing the Algorithm in GReAT

Translating Stateflow

Translating Simulink

Translating the Tank Level Control example

Summary

Conclusion

VII. RESULTS, CONCLUSIONS AND FUTURE WORK

Results

Requirement 1

Requirement 2

Requirement 3

Requirement 4

Requirement 5

Requirement 6

Requirement 7

Requirement 8

Requirement 9

Revisiting the Research Hypothesis and Completion Criteria

Conclusion

Future Work

Appendix A. ALGORITHM FOR SINGLE CARDINALITY PATTERN MATCHING......... 165 B. ALGORITHM FOR FIXED CARDINALITY PATTERN MATCHING........... 166

C. ALGORITHM FOR VARIABLE CARDINALITY PATTERN MATCHING... 167

D. FORMAL SEMANTICS OF GREAT

E. CONFIGURATION ASPECT OF UMT

F. THE SIMULINK/STATEFLOW TO HSIF TRANSLATION ALGORITHM.... 183 REFERENCES

–  –  –

1. Comparison of Various MIC Tools

2. Comparison of Graph Transformation Tools

3. Concrete Syntax of the pattern graph and the rule interface

4. Mapping Simulink blocks to sub expressions

5. Compilation of different projects developed in GReAT

–  –  –

1. An Example Petri Net

2. Basic notations of UML class diagrams [36]

3. an Example IDEF3 Process Description Diagram [30]

4. Design Methodology Management using Ptolemy [31]

5. The MIC Development Cycle [1]

6. A NLC production

7. Application sequence of a production

8. A NCE production

9. Two graphs with embedding and the result of their substitution [54]

10. Hyperedge production

11. An example to demonstrate the hyperedge production

12. DPO production

13. SPO production and example

14. A production in the PROGRES system

15. Metamodel of hierarchical concurrent state machine using UML class diagrams..72

16. Metamodel of a simple finite state machine

17. A metamodel that introduces cross-links

18. Non-determinism in matching a simple pattern

19. Pattern specification with cardinality

20. Pattern with different semantic meanings

21. Conflicting match for the tree semantics

22. Hierarchical patterns using set semantics

–  –  –

24. Variable cardinality pattern and family of graphs

25. An example rule with patterns, guards and attribute mapping

26. UML class diagram for the abstract syntax classes of GReAT: The core transformation classes

27. UML class diagram for the abstract syntax classes of GReAT: The interface........92

28. Firing of a sequence of 2 rules

29. Rule execution of a Block

30. Sequence of execution within a Block

31. Rule execution sequence of a ForBlock

32. Execution of a Test/Case construct

33. Execution of a single Case

34. Inside the execution of a Test

35. A non-deterministic execution sequence

36. Pivoted Matching

37. Transformation Rule with pivot

38. Sequence of rules with passing of previous results

39. Concrete syntax of the different expressions in GReAT

40. GR: the abstract syntax of GReAT

41. High-level block diagram of GRE

42. Block execution algorithm

43. For block execution algorithm

44. Test execution algorithm

45. Algorithm for rule execution

46. Performance graphs for Df Fdf

47. Performance graphs for Hsm Fsm

–  –  –

49. A tank with three valves

50. The ”true” (hybrid automata) state machine for the tank example

51. The StateflowPart Rule

52. The HSM2FSM rule

53. Inside the OR rule

54. ElevateChildOr rule

55. The StateSplitting rule

56. The SetImplicitValues Rule

57. Stages of Stateflow splitting

58. The domain of Turing machines

59. The top-level rule of Turing machine

60. Internals of RunMachine

61. Inside Q1 block, choosing action for current state and symbol

62. Action taken for a particular State, symbol pair

63. Transformation to make isomorphic copy of graph

64. Metamodel of the configuration aspect of UMT

–  –  –

UML – Unified Modeling Language MDA – Model Driven Architecture OCL – Object Constraint Language DSL – Domain Specific Language GPL – General Purpose Language GP – Generative Programming MIC – Model Integrated Computing GReAT – Graph Rewriting And Transformation GRE – Graph Rewriting Engine GRD – Graph Rewriting Debugger CG – Code Generator MoC – Model of Computation FSM - Finite State Machine.

CFSM - Codesign Finite State Machine.

TM – Turing Mcahine HPN - Hierarchical Petri Nets.

SDF - Synchronous Data Flow.

ADF - Asynchronous Data Flow.

OMG – Object Management Group AHEAD – Algebraic Hierarchical Equations for Application Design DSDE – Domain Specific Design Environment

–  –  –

GTDL – Graph Type Definition Language ER – Entity Relation KMF – Kent Modeling Framework GME – Generic Modeling Environment.

NLC – Node Label Controlled NCE – Neighborhood Controlled Embedding DPO – Double Pushout SPO – Single Pushout PROGRES – PROgrammed GRaph REplacement System PSRS – Programmed Structure Replacement System DSMDA – Domain-Specific Model Driven Architecture DSPIM – Domain-Specific Platform Independent Model DSPSP – Domain-Specific Platform Specific Model DSME – Domain-Specific Modeling Environment HCSM – Hierarchical Concurrent State Machine GSS – Grouped Set Semantics UMT – UML Model Transformer GR – Graph Rewriting UDM – Universal Data Model MSS – Matlabs Simulink and Stateflow HSIF – Hybrid System Interchange Format HA – Hybrid Automata

–  –  –

The evolution of programming languages shows a clear direction towards higher levels of abstraction. This evolution started from assembly languages, went on to procedural languages, then to object-oriented languages and now the state of the art is component-oriented languages and frameworks. In the same timeframe, top down approaches classified as Model Based Software Engineering [7] tried to develop highlevel graphical languages and generate assembly/machine code from them. These approaches attempted to bridge large semantic gaps between very-high-level semantic models and very-low-level languages. There were many challenges in such an enterprise and tool infrastructures and frameworks did not live up to expectations. This led to their failure to achieve the goals set by the community. This community found success in more rigorous domain-specific fields such as embedded systems where formal and graphical models were already in use. An example of such a success is Matlab’s Simulink/Stateflow [38] modeling language. With the advent of Unified Modeling Language (UML) and Model Driven Architecture (MDA) that advocate the use of models in software development, the communities were brought together and are producing promising results.

Languages can also be divided into textual and graphical categories. Graphical languages are usually impractical for general-purpose programming but can be useful in a limited context in specific domains. We believe that a mixed textual and graphical notation can be helpful in limited domains. For example, in the software development domain, the UML [3] specification has both textual (Object Constraint Language) and graphical (Use-Case Diagram, Class Diagram, etc.) notations. In hardware development domain, tool vendors [39] are now providing a graphical notation for the structural description of hardware while the behavioral description is still textual.

The primary reasons behind the limited success of Domain Specific Languages (DSLs) have historically been the following:

• DSLs are more expensive to create as the development cost and time is borne by a small user community

• Since there is a small user base, tools and support for a DSL is not at par with General Purpose Languages (GPLs) and

• The wide user base and longer life of GPLs helps make the language implementations robust and reliable.



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