visualization of transition systems

/department of mathematics and computer science

Visualization of Transition Systems

Hannes PretoriusVisualization Group

[email protected]/~apretori


Outline1. Transition systems2. Motivation3. Existing results4. Process5. Experiment 1: projection6. Experiment 2: simulation7. Experiment 3: clustering8. General conclusions


Transition systems• Used to analyze and verify systems• Explicit descriptions of system

behavior: – All system states– Transitions between these

• Describe systems whose states evolve over time


Transition systems


Motivation1. Facilitate insight:

– Tools and techniques to understand systems

– Address size and complexity

2. “Gestalt”:– Associate visual form with abstract

formalism– Enhances communication


Motivation• Frank van Ham’s

FSMView:– Shows global structure and

symmetries

• New approaches:– FSMView excels at what it

was designed for– Investigate techniques with

more focus on attributes (data, transition labels, …)

– More interaction


ProcessFormal

specification

LPE

LTS

Σ

Validate

Simulate

Specify

Real world


Experiment 1: projection

Σ

Validate

Simulate

Specify


• Data communication • Data is stored

– States: vectors of data values– Multidimensional points

Experiment 1: approach

[1,1,0,2]

[1,1,2,1][2,2,1,0]



Data Visualmapping

Visualmapping

Overview(dims)

2Dprojection

Visualmapping

Selectsubset


Experiment 1: overviewState

Dimension

1 2 1

1 2 1

0 1 2

2 0 1

s1 s2 s3

a

b

c

d


Experiment 1: projecting to 2D

s1 s2

s3


Experiment 1: demo


Experiment 1: results• Identify:

– Superfluous variables– Correlations spanning multiple dimensions– States with specific valuations of variables– Phases of behavior

• Interaction:– Coupling of two views important

• “Data-centric” view:– Does not reveal enough about behavior


Experiment 2: simulation

Validate

Simulate

Specify


Experiment 2: approach• Simulation:

– Quick and incremental checking of specification

• Text-based output:– Time consuming analysis– Not intuitive– Prone to human error


Experiment 2: approach• Automated parking garage• Custom plug-in:

– Maps current system state onto 2D floor plan

• Visual cues:– Lift position– Status of parking space (free, occupied,…)– Status of shuttles


Experiment 2: visual mapping

LiftShuttl

e

Occupied

Free

Conveyor belt


Experiment 2: demo


Experiment 2: results• Efficiency of analysis:

– Saves time– Intuitively clear and easy to follow– Identified and corrected serious errors early on

• Stakeholders: – Convinced colleagues of merit of visualization– Means to communicate abstract ideas to client

• Shows behavior


Experiment 3: clustering

Σ

Validate

Simulate

Specify


Experiment 3: approach• Directed graph:

– Reduce size and complexity

• Associate meaning with states and transitions:– Reduce in meaningful way– Ability to take behavior into account


Experiment 3: initial ideas• Linear representation:

– Predictable– Suggests ordered analysis– Effective way to show patterns

• Initial clustering:– Ranking (BFS, DFS)– Based on local behavior

(fan-in, fan-out of transition labels at every state)


Experiment 3: initial ideas


Experiment 3: approach• Every state is annotated:

– Tuple with coordinates– Every coordinate has associated type– Every type has a domain

• Partition states based on equivalence relation:

– Select subset of coordinates– Cluster domain of associated type



Defineeq. rel.

Data

Eq. rel

Cluster Clustereddata

Visualmapping

Selectioninfo.

Vis.

Select



First partitioning: coordinate i

Original transition system



Second partitioning: coordinate j

Original transition system


Experiment 3: approach• Allows for clustering based on:

– Existing attributes– Derived attributes

(structural properties, equivalence classes, …)

– Construct hierarchy of clusters


Experiment 3: demo


Experiment 3: results• Definition of equivalence relation on the fly:

– Select subset of components– Cluster type domains

• Assumption: – User associates meaning with components

• Combination of clusters and hierarchy promising

• Work in progress: – Interaction– Derived attributes (behavior-related)


General conclusions• Experiments:

– Gap between abstract and real world– Address sub-problems– Better understanding of problem

(focus on behavior, clustering more general, …)• Looking ahead:

– Include insights in current tool– Add more functionality to current tool

(annotation, selection, may- and must-transitions)


General conclusions• Open questions:

– How to move from one clustering to the next

– Simulation works really well, how to generalize?

– User requirements

?

?


Questions

visualization of transition systems

Documents