regular extrapolation of behavioural models treating systems lacking specification regular...

Regular Extrapolation of Behavioural Models Regular Extrapolation of Behavioural Models

Treating Systems Lacking SpecificationTreating Systems Lacking Specification

Bernhard SteffenAndreas HagererHardi HungarOliver NieseTiziana Margaria

Bernhard Steffen Turku 19.-30.8.2002

Moderated, Regular ExtrapolationModerated, Regular Extrapolation

Extrapolation

Hypothesis Building beyond known facts

Regular

Extrapolation-Universe: Extended Finite Automata

Moderated

The Extrapolation Process requires targeted

interaction


PlanPlan

1st Lesson: Systems Lacking Specification

2nd Lesson: Controlling the State Space

3rd Lesson: Integrating Expert Knowledge

4th Lesson: Validation and Diagnosis

5th Lesson: Perspectives


PlanPlan


Application Scenario: CTI Systems

Computer/Telephony Integrated Systems

Model Construction by Extrapolation






Computer/Telephony Integrated SystemsComputer/Telephony Integrated Systems

ISDNNetwork

Switch

Model-Generator

Applikations-PCs

Applikation-Server

LAN


In more Detail In more Detail

LAN

Model Generator

Call Center Clients

Call Center Server

CSTA II/III

TAPI

TAPI

Rational RobotRational Robot

Intranet/Internet

Hipermon

HUSIM

CorNet

ISDNNetwork


Models in our ScenarioModels in our Scenario

AbstractAbstract representation of the protocol-level behaviour.

Abstraction typically concerns – concrete names of entities:

replace ”102”, ”500” by A1 , A2– details like time stamps etc.

{ invokeID = 58391,

operation-value = 21 (cSTAEventReport),

{eventSpecificInfo. ... .hookswitch

{deviceId.dialingNumber = “500”

hookswitchOnHook= TRUE,

...

timestamp = “20001010095551”

} }}}

{obsEvent

deviceId = A1

switchOnHook,

...

}}


Sketch of the Model Structure Sketch of the Model Structure

Models comprise state changes as well as UPN-UPN- and

CSTA-ObservationsCSTA-Observations.

Sys_Info

Sys_Info

obs_CSTA

obs_CSTAupnOffHook

obs_CSTA

obs_CSTA

{

{deviceId = A1 hookswitchOnHook,

...

}}

device A1 display(line 1, ...) LEDs: (1,on) (2,off) ......


A First Practical ModelA First Practical Model


Conceptual Model Structure:Conceptual Model Structure: MIOLKTSMIOLKTS

Modal: for loose specification

Input: must always be enabled

Output: is system-controlled

Labelled: complex annotations

Kripke: state-oriented

Transition Systems: action-oriented


Iterative Model ConstructionIterative Model Construction

Collection

Refinement

Validation

Abstraction

Folding


Model before FoldingModel before Folding


AbstractionAbstraction

Abstraction of Instances:

From concrete objects to roles

Hiding:

Suppression of unimportant details

Partial Order Reduction:

Flexibilization of action sequencing


Partial Order ReductionPartial Order Reduction

Independent actions commute:

– Model Completion

– Size Reduction

offHook_A

onHook_A

offHook_B

onHook_B

offHook_A

offHook_B

onHook_A

onHook_B

offHook_B

offHook_A

onHook_A

onHook_B


FoldingFolding

State-oriented:

based on similarity of state descriptions

Path-oriented:

based on similar behavioural options

Model Collapse:

based on similar behavioural properties


State-Based Folding State-Based Folding

=


RefinementRefinement

Property-Oriented Expansion:

separates states

Synthesis-based Model Expansion:

enriches the behavioural potential

Regression-Expansion:

lifts the level of detail


Property-Oriented ExpansionProperty-Oriented Expansion

Example: Feature Group Call

AB AB

BB

AB

B+C, B-C

C: Call AC: --

AB

B

B-C

C: --

AB

B

AB

C: Call A

B+C

AB


Reference Model

device A1 display(line 1, ...) LEDs: ... (8,off) ...

...

Regression ExpansionRegression Expansion

Effect: More detailed test evaluation

Test Graph

check-Display

passed failed

Effect

056784 >an h?


Error DiagnosisError Diagnosis

Scenario: Regression Test

• Model refines the test

• improved error diagnosis

??Test Run

upnDigitobsEvent1 obsEvent2

checkDisplay

Modelpath

queuedEvent

upnDigit checkDisplayobsEvent obsEvent


ValidationValidation

Branch Completion:

Comparison of the branch potential

Cycle Identification:

Refinement of the folding approach

Model Checking:

Validation of (temporal) constraints


Goal-Oriented TestingGoal-Oriented Testing

Model ValidationValidation and CompletionCompletion with automatic consistency checks

??Cycle ValidationCycle Validation

??Branch CompletionBranch Completion


Usage of extrapolated ModelsUsage of extrapolated Models

Test Generation

Investigation of Coverage

Test Evaluation

Monitoring

Simulation and Validation

Change ManagementChange Management


Typical MethodsTypical Methods

Program and Data Flow Analysis

Abstract Interpretation

Model Checking

Model Synthesis

Machine Learning


ConclusionsConclusions

`Classical´ validation and analysis methods become

available for Legacy-Systems.

Precision or Completeness is unnecessary for practical

usage.

Model Construction automatically extends Expert

Knowledge.

Also here: The `Waterfall´ becomes an iterative

process.


PlanPlan



The State Explosion Problem

Abstraction and Projection

Concise Representations




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