Intro to Modeling

CS 622/722Reflective and Adaptive Systems

November 4, 2008

MDE and DSM slide help from Jean Bezivin and MetaEdit+…MIC slide help taken from various ISIS talks…

Agenda Today

Quiz 7 Intro to MDE/MDA Intro to MIC slides HW 2 Due Nov 18

Next Tuesday Quiz 8 Hands-on-lab: GME

November 18 SoftCom research in this area

November 25 Not sure?

December 2 Exam 2

From OO to MDE (Model-Driven Engineering) Object technology realized some promises but failed to

achieve others Stopping the search for generality by unification "everything

is an object" may be one of the causes for this Model engineering is making many promises today

Will it be able to deliver correspondingly? Sticking with the principle that "everything is a model"

seems a good way to make progress

Objects

Models

1980 2000 2020

Promises Delivery

Evaluation Promises

Delivery Evaluation

Separating the platform dependent and independent parts of systems

We don't want to pay such a high price anymore for simply moving our information system to a new middleware platform (COM, CORBA, Java, HTML, XML, DotNet, etc.) when our business system stays stable.We are prepared to pay a last price for building the abstract models of our business and services that will guarantee us against technological obsolescence.From there, any platform provider will also have to provide the mapping solutions from standard business models before we buy.

The origins of M

DA

November 2000

Model Driven Architecture (MDA)

PIM to PSM

CORBA

Java/EJB

C#/DotNet

Web/XML/SOAP

PIM

etc.

Platform-IndependentModelMulti-target

code generation

+ SVG, GML, Delphi, ASP, MySQL, PHP, etc.

data grid computingpervasive computingcluster computing

SMIL/Flash

Question

But what is a model?

(Naïve introduction)

Next Few slides adapted from Jean Bezivin….

A very popular model: geographical maps (thanks to Jean Bezivin for this idea)

Models

repOfSystem

2000 Census Map

Aerial Map

Model Road Map

The System

1819 City Plan

Every map has a legend

legend = metamodel

Model

conformsTo (c2)

Metamodel

Another Notation (DSL)

Model

Metamodel

c2

Music notation

Sheet music

Executable Model

Power Tab Editor

Two Views:

Traditional notes

Guitar tab

Assisted Drawing Tools (e.g. MS/Visio)

conformsTo(c2)Metamodel Model

(thanks to Jean Bezivin for this slide)

The Rational/Booch Template

Concrete syntax

Schema definitions: Going “Meta”

Domain-SpecificModeling

ProgrammingLanguage Definition

Database SchemaDefinition

Schema definitionMetamodel for aspecific domain(e.g., Petri Net)

Grammar for a specificlanguage (e.g., Java)

Table, constraint, and storedprocedure definitions for a

specific domain (e.g., payroll database)

Schema instance

Domain model (e.g., Petri Net model of a teller

machine)

A program written in aspecific language

Intension of a database at aspecific instance in time

(e.g., the June 2006 payrollinstance)

Schema execution Model InterpreterLanguage

compiler/interpreter

Transactions and behavior ofstored procedures

in an executing application

Characteristics of Modeling Languages Each model conforms to its

metamodel A model is a representation

of a system satisfying substitutability For each question that can be

asked of the system, the model produces the same answer Not true for me and road maps!

(from Jean Bezivin)

Observation• “One size fits all” approach is appearing to be

inadequate for many end-user needs• Too complex and contains “kitchen sink” approach

providing things most users do not need (UML)• Current trend is to provide “domain-specific”

modeling languages that are customized to a specific domain • Notations and abstractions are exactly what the

users expects; focused on problem space, not technology solution space

• But, how to create such modeling languages and environments?• Expensive to create from scratch for each domain

Typical Situations (only some)Visio Stencil

Visio Drawing

c2

Form definition

Form content

c2

XML Schema

XML Document

c2

Legend

Map

c2

File Structure

File content

c2

UML MM

UML Model

c2

Music Theory

Partition

c2

Grammar

Program

c2

DB Schema

DB Content

c2

Agenda

We Need Precise and Consensual Definitions

Next Few slides adapted from Jean Bezivin….

Each model conforms to its reference model

Structural definition of a model Definition 1. A directed multigraph G = (NG, EG, G)

consists of a set of distinct nodes NG, a set of edges EG and a mapping function

G: : EG NG x NG

Definition 2. A model M = (G, , ) is a triple where: G = (NG, EG, G) is a directed multigraph is itself a model, called the reference model of M,

associated to a graph G = (N, E, ) : NG EG N is a function associating elements (nodes

and edges) of G to nodes of G (metaElements)

There are usually several kinds of models

Definitions

Definition 3. A metametamodel is a model that is its own reference model (i.e., it conforms to itself).

Definition 4. A metamodel is a model such that its reference model is a metametamodel.

Definition 5. A terminal model is a model such that its reference model is a metamodel.

The global picture

repOf

representation ofsystem S terminal

model M

metamodelMM

c2

metametamodelMMM c2

c2

the UML MetaModel

Class Attribute*

1

a UML Model

Client

Name : String

Illustration with OMG standards

M2

M1

the MOF

Class Association

source

destination

M3

c2

c2

c2

metamodel

model

"the real world"

meta-metamodel

The MOF

The UML metamodel

Some UML Models

Various usagesof these modelsM0

M1

M2

M3

Utilization definition

The objective here is to define the possible usages of a model.

Definition 6. A system S is a delimited part of the world considered as a set of elements in interaction.

Definition 7. A model M is a representation of a given system S, satisfying the substitutability principle (see below).

Definition 8. (Principle of Substitutability). A model M is said to be a representation of a system S for a given set of questions Q if, for each question of this set Q, the model M will provide exactly the same answer that the system S would have provided in answering the same question.

repOfrepresentation ofsystem S model

M

Principle of limited substitutability according to Minsky"If a creature can answer a question about a hypothetical experiment without actually performing it, then it has demonstrated some knowledge about the world. … We use the term "model" in the following sense: To an observer B, an object A* is a model of an object A to the extent that B can use A* to answer questions that interest him about A. …

It is understood that B's use of a model entails the use of encodings for input and output, both for A and A*.If A is the world, questions for A are experiments. ...A* is a good model of A, in B's view, to the extent that A*'s answers agree with those of A's, on the whole, with respect to the questions important to B. …"

Marvin L. MinskyMatter, Mind and Models Semantic Information Processing, MIT Press, 1968

The Modeling Tool World

Vanderbilt/ISIS work on MIC and the GME Eclipse GMT project (CIS 693/793) MetaCase MetaEdit+ Microsoft Software Factory Concept

Model Integrated Computing

Computer-Based Systems• CBS characteristics

• Hardware and software tightly coupled• Dynamic operating environment• Critical to the enterprise• Long lifespan• Multi-disciplinary development

• Examples• Embedded systems• Process monitoring, analysis• Manufacturing execution systems

CBS design is highly non-trivial

IEEE International Symposium on the Engineering of Computer-Based Systems (ECBS),

“Small changes in requirements entail large changes in the structure and configuration.”

•Digital Fire Control/NAV

•PT-PT Wiring

•Mechanically Controlled Sensors/FLT Controls/ Displays

•Crew-Dominated Operation

•Functionally Integrated Data Processing

-NAV/WD/Air Data Sensors

-Flight Control

•Beam Steering Sensors

•Fly By Wire

•Dedicated Digital Processing

•Crew-Assisted Operations

- Weapon Delivery

- Automated TF/TA

- EW Response

•Aircraft-Wide Information Integration

- Sensors/Stores/ Vehicle/

Propulsion

•Modular Electronics

•Massive Data Bases

- Terrain, Threat

•Digital Sensor Processing

- Sensor Fusion

- Hyperspectral Imaging

•Integrated Diagnostics/

System Fault Tolerance

•System Data Security

•Limited UAV Autonomy

•Platform Exploitation of Global Information

- Information Mining

- At-A-Distance

Reconfiguration

•Autonomous Vehicle Emphasis

- Air & Space

•Air Crew/ Ground Crew Monitoring & Management

•Automated Functions

- ATR (Multi-Sensor)

- Failure Prognostics

- Route/ Sensor/ Weapon/

Vehicle Coordination

- Bistatic Sensing

(Air/ Space)

- Threat Evasion

DEDICATED SUBSYSTEMS

FEDERATED SUBSYSTEMS

INTEGRATED SYSTEMS

SYSTEM of SYSTEMS

1958 1950’s - 60’s 1990’s - 00’s 2000 1970’s - 80’s

Embedded Software: Increasing Integration Role

64 KB

1 MB

1 GB

100 MB

Radar

Comm

EW

Integrated Avionics

Mission

Radar

Comm

NAV

Mission

Federated Avionics

Radar

Comm

NAV

Independent Avionics

Advanced Avionics

Source: AFRL

Evolution of Avionics Systems:

Components as Imagined (McIlroy)…

But: Responsibility for design integrity is in the hands of each system integrator

(Semantics problem) “Side effects” of component interactions turn up in the system integration

labs (or in the field) (Compositionality Problem)

Component Library

ApplicationsApplications

$ $ $

Notice the $ signs!

Composability Problem

Subsystem D Subsystem E Subsystem F

Subsystem A Subsystem B Subsystem C

Composability: Ability to link subsystems so that properties established at subsystem levels hold at the system level

But physical requirements crosscut functional component boundaries, which

weaken or destroy composability

Current technology focuses on functional composition...

EmbeddedSoftware

mCDMA

ROM

FPGA DSP

Process

Process

Pro

cess

EmbeddedSoftware

Computing as system integrator: computing is tightly integrated with physical environment

Due to its integration role, system-wide constraints and assumptions accumulate in software:• design assumptions • internal/external integrity constraints • usage assumptions ..scattered all over the software…undocumented

Possible solution:• constraints and assumptions need to be explicitly modeled • consistency needs to be checked and maintained during composition (even runtime)

Semantics Problem

Model-Integrated Computing

Technology for using

– domain-specific modeling and– model-based generators

to compose systems on various platforms

Arguments and Proposals

MIC proposes a two-level approach:1. Domain-specific modeling tools for creating domain-specific

models2. Modeling tools represented by, and built from, metamodels

• No one modeling language satisfies the requirements of all CBS’s

• UML often not helpful in some domains, despite efforts

• No single engineering discipline exists for CBS design

• Common language should be that of the domain, not computer engineering

• Integration of models and tools is necessary for CBS analysis and synthesis

• Modeling tool evolution must be easy, predictable, and safe

• Different categories of users; domain experts who may not be programmers

Domain-Specific Modeling (not UML)

Categories of End-Users

AdminAssistants

Businessman

Auto Factory Worker

Scientist

Spreadsheet

BusinessQuery Systems

Modeling Language

DSL forPhysics

Model Integrated Computing

MIC is a technology for Modeling computer-based systems Synthesizing applications from the models Integrating new and legacy applications Evolving computer-based systems

Logical separation of design, implementation True end-user programmability

Effective integration solutions are possible due to the integrated nature of the models

Implementing MIC• Domain-specific, graphical

design tools• Automated model translation

creates• Data streams for existing

simulation, analysis tools• Configuration info for system

components• Executable software

applications• User-controlled system

evolution• Domain experts update

models, regenerate system

12/15/97

Components

Domain Modeling

Mapping/Synthesis

Integration Platform

Metamodeling and Modeling

ModelMetamodel

OCL Constraints1. Abstract & Concrete Syntax

2. Static Semantics

3. Visualization

Specification of Domain Specific Modeling Languages (DSML)

ConcreteSyntax

C

AbstractSyntax

A

SemanticDomain

SSemantics

parses to parses to parses to

Concepts

Relations

Well formed-nessrules

Mathematical abstraction for specifying the meaning of models

Notation forrepresenting models

L = < C, A, S, MS, MC>

MC

MS

Modeling Language Definition

Notation forrepresenting models:E.g.: Block Diagram

Concrete

Syntax

C

Abstract

Syntax

A

Semantic

DomainS

Semantics

parses to parses to parses to

MC

MS

Mathematical abstraction

for specifying the meaning of models

But What About S?

Signal Flow Language (SF)Concepts, Relations

Well formed-ness rules:

Self.InputPorts()forAll(ipip.src()forAll(x1,x2x1=x2))

UML-CD/OCL

Semantics via Meta-Modeling

Concrete

Syntax

Abstract

Syntax

Semantic

DomainSemantics

parses to parses to parses to

Meta-modeling language withwell-defined semantics

Concrete

Syntax

Abstract

Syntax

Semantic

DomainSemantics

parses to parses to parses to

Represented by

Meta-modelSemantics

DSML

Meta-modelStructural

SemanticsDSML:StateFlow

DOMAIN-MODEL

META-MODEL

Meta-Model of StateFlow using uml/OCLas meta modeling language.

Semantics via Translation

Concrete

Syntax

Abstract

Syntax

Semantic

DomainSemantics

parses to parses to parses to

Concrete

Syntax

Abstract

SyntaxSemantics

parses to parses to parses to

Semantic

Domain

translator

DSML

Semantics

Modeling language with

well-defined semanticsSynchronous Dataflow (SDF)

translatorBehavioral

Semantics

ASF

Hierarchical Signal Flow (HSF)

Lee, Sangiovanni-Vincentelli

Model-Based Generators

Models: stored as directed, attributed graphs

Generators:traverse/transform

Targets:

executable models

analyzable model

Synchronous Dataflow

Petri Net

Domain-Specific Modeling at ISIS:Model Integrated Computing• The Generic Modeling Environment (GME) is a domain-

specific modeling tool• It can be utilized in many different domains by providing

a meta-level paradigm description• Paradigm describes all of the entities of the domain,

as well as valid relationships• An end-user first loads the domain paradigm and then

constructs new models in that domain

See November 2001 issue of IEEE Computer

ModelInterpretation

Model Interpreters

Models

Modeling Environment

ApplicationDomain

App1

App2

App3

Application Evolution

Environment Evolution

MetamodelingInterface

Metamodel Definition

Meta-LevelTranslation

Model Builder

Model Integrated Computing (MIC) with GMEM

etamodel

Model

Interpreter

void CComponent::InvokeEx(CBuilder &builder,CBuilderObject *focus, CBuilderObjectList &selected, long param) {CString DMSRoot = "";DMSRoot = SelectFolder("Please Select DMSRoot Folder:");if (DMSRoot != "") {DMSRulePath = DMSRoot + RULESPATH + "Rules\\";MSRuleApplierPath = DMSRoot + RULESPATH + "RuleApplier\\";AfxMessageBox("DMSRulePath = " + DMSRulePath , MB_OK);CString OEPRoot = "";OEPRoot = SelectFolder("Please Selec

DEFINE

INTERPRET

The Generic Modeling Environment (GME) adopts the MIC approach and provides a plug-in mechanism for extension.

GME Architecture

GME

Persistent Storage Model database

Storage Interface

Editor Core

Graphical User Interface

Constraint Checker

Meta models

Model data

Domain model

[download site: http://www.isis.vanderbilt.edu/Projects/gme]

Internal Structure Use

MICApplications

Saturn Site Production Flow

Saturn Site Production FlowProduction Flow

• Flow of raw materials, parts, sub-assembly through the operations

• A complex network of operations and buffers

• Production flow very critical to throughput

Saturn Site Production FlowData Collection and Storage

• Real-time collection• Nearly 80,000 points• Large-scale, distributed system• Storage of historical data• Hundreds of distributed clients

• LAN and WAN• Real-time and historical data

Saturn Site Production FlowAnalysis and Visualization

• Flexible access to real-time/historical data• Visualization• Bottleneck identification• What-If analysis• Data trending• Interoperation with COTS• Reports/charts generation

Real-Time Viewer

Saturn Site Production FlowSoftware Engineering Issues

• Large-scale, complex, heterogeneous and continually changing environment

• Distributed, real-time application• Changing requirements• Integration with the plant• Integration of diverse software sub-systems

• Custom and COTS• Maintenance• Evolution

New Capabilities, Benefits

• 10-100 X decrease in system integration cost

• Enables evolution and adaptation of complex software

• Provides “true” end-user programmability

Domain-Specific Modeling with MetaEdit+

What we need to define for DSM?• Metamodel

• Modeling concepts, their properties, rules and correctness constraints

• Symbols• Notation & representation of diagram, matrix or table

• User interface of a modeling tool• Dialogs, toolbars etc

• Generators• Code, checkings, build, metrics, documentation etc.

• Connectivity with other tools• Help and tutorials• Share the DSM to its users• Reflect to models already made

Proper tool creates many of these automatically

Concepts Symbols

GeneratorsRules1 2 3 4

Steps for implementing DSM

1. Design domain concepts (1)• Map modeling concepts accurately to domain

concepts• What information is stored with each concept

• In MetaEdit+ metamodels are defined in forms or in graphical models• below a sample of metamodeling in a form (left)

• Metamodels can be immediately tested in modeling

1. Design domain concepts (2) Metamodel can be also described graphically

e.g. metamodel of class diagram (partial below)

2. Define domain rules Language semantics and rules

defined as they exist in the domain

In MetaEdit+ rules are expressed as data

Examples of rule types: Links between concepts Layering abstractions Occurrency, uniqueness

3. Draw symbols (notation)• Define symbols illustrating as well as possible the

corresponding domain concept’s natural “visualization” • In MetaEdit+ symbols can be drawn with Symbol Editor

or exported as bitmaps and svg (vector graphcis)

4. Implement generators• Generator translates the models into a required output• In MetaEdit+ generators are defined using Generator Editor • Generators can be defined also externally with any

programming language accessing models via API or XML• Generator Editor (below) is linked to the metamodel and

provides generator debugger and traces from generated code to models

MetaEdit+ delivers automatically modeling tools for the metamodel• Editors (diagram, matrix, table), browsers, generators, multi-

user, multi-project, multi-platform environment• Method easy and safe to maintain and share

• Shared new language versions, updates models already made

Case Study : Enterprise apps in smartphones• Symbian/Series 60 for enterprise application

development• Platform provides basic services• Modeling language to define application logic

using basic widgets and services• Code generator produces 100% of

implementation• Complete chain from model to running app

• Symbian/Series 60 application development• Platform provides basic services• Modeling language to define application logic using basic

widgets and services• Code generator produces 100% of implementation• Complete chain from model to running app • Libraries possible to integrate

Case Study : Enterprise apps in smartphones

Generator definition

Function calls – Series 60/Python

def Note3_2227(): appuifw.note(u"Registration made", 'conf') return Stop3_983

def Note3_6109(): appuifw.note(u“Registration cancelled", ‘info') return Stop3_983

def Note3_2543(): appuifw.note(u“Conference registration: Welcome", ‘info') return Popup_menu3_2520

def Stop3_983():# This applications stops here return appuifw.app.set_exit

...

Generator output

Report '_Note'/* Produces Note code */'def '; type;oid; '():'; ‘ appuifw.note(u"';:Text or code; '", '''; :Note type; ''')'; subreport; '_next element'; run;endreport

Report '_next element'/* reports next flow element*/do ~From>Flow { do :Function used {' '; :Function name;} '('; :Code; ')'; do ~To.() {‘ return '; subreport; '_Internal name'; run;}}endreport

Generator definition

Summary• Productivity can be improved by raising the

abstraction beyond coding• 5-10x faster than manual approaches

• Automation can be applied effectively if bothmetamodel and generators can be customized

• MetaEdit+ provides a cost-effective way to create automation

• Building DSM is great fun for experts• MetaEdit+ is tried and proven technology

• Hundreds of DSM implementations