Jri Vain Dept. of Computer Science Tallinn University of Technology - - PowerPoint PPT Presentation

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Jri Vain Dept. of Computer Science Tallinn University of Technology J.Vain ...Reactive Planning 1 Testing...Abo, Feb 3, 2012 Vain, Jri; Kramees, Marko; Markvardt, Maili Online testing of nondeterm inistic system s w ith

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Jüri Vain

  • Dept. of Computer Science

Tallinn University of Technology

J.Vain “...Reactive Planning Testing...”Abo, Feb 3, 2012 1

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J.Vain “...Reactive Planning Testing...”Abo, Feb 3, 2012 2

Vain, Jüri; Kääramees, Marko; Markvardt, Maili Online testing of nondeterm inistic system s w ith reactive planning tester. In: Petre, L.; Sere, K.; Troubitsyna, E. (Eds.). Dependability and Computer Engineering : Concepts for Software-Intensive Systems. Hershey, PA: IGI Global (2012), pages 1 1 3 -1 5 0 .

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Outline

 Preliminaries

 Model-Based Testing  Online testing

 Reactive Planning Tester (RPT)  Synthesis of the RPT  Performance issues  Test execution environment dTron  Demo

J.Vain “...Reactive Planning Testing...”Abo, Feb 3, 2012 3

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What is testing for?

 to check the quality (functionality, reliability,

performance, … ) of an (software) object

  • by performing experiments
  • in a controlled way

In avg. 10-20 errors per 1000 LOC 30-50 % of development time and cost in software

4 J.Vain “...Reactive Planning Testing...”Abo, Feb 3, 2012

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What is a Test?

Software under Test (SUT) Test Data Output Test Cases

Correct result?

Oracle

5

J.Vain “...Reactive Planning Testing...”Abo, Feb 3, 2012

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J.Vain “...Reactive Planning Testing...”Abo, Feb 3, 2012 6

Model- Based Testing (typically)

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Specifics of testing embedded systems

J.Vain “...Reactive Planning Testing...”Abo, Feb 3, 2012 7

– non-determ inism – partial observability – RT constraints – dependability  test coverage issues

How to address them in testing? Real environment in the loop!

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Option 1: Offline vs Online testing

 Offline testing

 Open “control” loop  Test is not adaptive to outputs or timing of SUT  Test planning – result analysis loop is long

 Online testing

 Flexible, test control is based on SUT feedback  One test covers usually many test cases

J.Vain “...Reactive Planning Testing...”Abo, Feb 3, 2012 8

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Online testing

 Group of test generation and execution

algorithms that

 compute successive stimuli at runtime directed

by

 the test purpose and  the observed outputs of the SUT

J.Vain “...Reactive Planning Testing...”Abo, Feb 3, 2012 9

Tester SUT

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Online testing

 Advantages:

 The state-space explosion problem is reduced

because only a limited part of the state-space needs to be kept track of at any point in time.

 Drawbacks:

 Exhaustive planning is diffcult due to the

limitations of the available computational resources at the time of test execution.

J.Vain “...Reactive Planning Testing...”Abo, Feb 3, 2012 10

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Option 2: Test scripting vs model-based generation

J.Vain “...Reactive Planning Testing...”Abo, Feb 3, 2012 11

 Scripting:

  • does not need SUT modelling

+

  • sensitive to human errors
  • inflexible, needs rewriting even with small

changes of SUT specs

  • hard to achieve test coverage
  •  Model-based generation:
  • considerable SUT modelling effort
  • correctness of tests is verifiable

+

  • easy to modify and regenerate

+

  • clear characteristics of coverage

+

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Model-Based Testing

 Given

 Model of the SUT specification  System Under Test (SUT),  The test goal (in terms of spec. model elements)

 Find

 If the SUT conforms to the specification in terms

expressed in the test goal.

J.Vain “...Reactive Planning Testing...”Abo, Feb 3, 2012

NEEDED!

  • Sufficiently rich modelling formalism,
  • Supporting tool set.

12

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Model-Based Testing

 We assume formal specs as:

 UML State Charts  Extended Finite State Machines  MSC  OCL

 etc.

 UPTA -Timed Automata (timing, parallelism,

test data)

J.Vain “...Reactive Planning Testing...”Abo, Feb 3, 2012 13

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”Relativized Real-Tim e i/ o conform ance” Relation

Spec = UppAal Timed Automata Network: Env | | IUT Timed Trace: i1.2½ .o1.3.o2.19.i2.5.i3

Expected system reaction Input event ordering

16 J.Vain “...Reactive Planning Testing...”Abo, Feb 3, 2012

Online MBT architecture: UppAal-TRON

[ www.uppaal.com]

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Bottleneck of online MBT: planning Spectrum of planning methods

 Random walk (RW): select test stimuli in random

 inefficient - based on random exploration of the state space  leads to test cases that are unreasonably long  may leave the test purpose unachieved

 RW with reinforcement learning (anti-ant)

 the exploration is guided by some reward function

 ........  Exploration with exhaustive planning

 MC provides possibly an optimal witness trace  the size of the model is critical in explicit state MC  state explosion in "combination lock" or deep loop models

17 J.Vain “...Reactive Planning Testing...”Abo, Feb 3, 2012

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Bottleneck of online MBT: planning Spectrum of planning methods

 Random walk (RW): select test stimuli in random

 inefficient - based on random exploration of the state space  leads to test cases that are unreasonably long  may leave the test purpose unachieved

 RW with reinforcement learning (anti-ant)

 the exploration is guided by reward function

 ........  Exploration with exhaustive planning

 MC provides possibly an optimal witness trace  the size of the model is critical in explicit state MC  state explosion in "combination lock" or deep loop models

???

18 J.Vain “...Reactive Planning Testing...”Abo, Feb 3, 2012

Full space 1 step ahead Zero planning

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Bottleneck of online MBT: planning Spectrum of planning methods

 Random walk (RW): select test stimuli in random

 inefficient - random exploration of the state space  test cases that are unreasonably long  may leave the test purpose unachieved

 RW with reinforcement learning (anti-ant)

 the exploration is guided by some reward function

 ........  Exploration with exhaustive planning

 MC provides possibly an optimal witness trace  the size of the model is critical in explicit state MC  state explosion in "combination lock" or deep loop models

Planning w ith adaptive horizon!

19 J.Vain “...Reactive Planning Testing...”Abo, Feb 3, 2012

Full space 1 step ahead Zero planning

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Reactive Planning in a Nutshell (1)

 Instead of a complete plan, only a set of

decision rules is derived

 The rules direct the system towards the

test goal.

 Based on current situation evaluation just

  • ne subsequent input is computed at a

time.

 Planning horizon is adjusable

J.Vain “...Reactive Planning Testing...”Abo, Feb 3, 2012 20

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Reactive Planning: Planning cycle

 Identify current state of SUT:

 Observe the output (or history) of the SUT

 Pick the next move:

 Select one from unsatisfied test (sub-)goals Gi

 Compute the best strategy for Gi:

 Gain function guides the exploration of the model

(choose the transition with the shortest path to the (sub-)goal Gi)

J.Vain “...Reactive Planning Testing...”Abo, Feb 3, 2012 21

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J.Vain “...Reactive Planning Testing...”Abo, Feb 3, 2012

Reactive Planning Tester in a Nutshell (2)

State model of the IUT Test goal Reactive Planning Tester (RPT) model Reachability Analysis Reactive Planning Tester (RPT) implementation SUT

Offline phase Online phase

Stimuli / responses RPT is another state model

22

RPT autonomously generates stimuli to reach the goal

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Constructing the RPT model

J.Vain “...Reactive Planning Testing...”Abo, Feb 3, 2012 23

Model of SUT Test goal Model

  • f RPT

Test strategy parameters:

  • planning horizon
  • timeouts

Synthesis of RPT Extraction

  • f the

control strcture Constructing gain guards (GG)

Constructing gain functions

Reduction

  • f GG
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RPT: Key Assumptions

 Testing is guided by the (EFSM) model of the

tester and the test goal.

 Decision rules of reactive planning are

encoded in the guards of the transitions of the tester model.

 The SUT model is presented as an output

  • bservable nondeterministic EFSM in which all

paths are feasible1.

1 - A. Y. Duale and M. U. Uyar. A method enabling feasible

conformance test sequence generation for EFSM models. IEEE Trans. Comput.,53(5): 614–627,2004.

J.Vain “...Reactive Planning Testing...”Abo, Feb 3, 2012 24

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Example: Nondeterministic FSM

s2 s3 e6: i6/ o6 e7: i7/ o7 s1 e0: i0/ o e1: i0/ o

1

e2: i2/ o2 e3: i3/ o

3

e4: i3/ o

4

e5: i5/ o5 i0 and i3 are output observable nondeterministic inputs

J.Vain “...Reactive Planning Testing...”Abo, Feb 3, 2012 25

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Encoding the Test Goal in SUT Model

 Trap - a boolean variable assignment

attached to the transitions of the IUT model

 A trap variable is initially set to false.  The trap update functions are executed (set

to true) when the transition is visited.

J.Vain “...Reactive Planning Testing...”Abo, Feb 3, 2012 26

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Goal directed testing: The power of traps

 Several goals can be expressed  all/ selected transitions  transition sequences (traps with

reference to other traps)

 advanced goals using auxiliary variables,

consequent transitions, repeated pass, …

 traps with 1st order predicates data variables

 Properties not expressible by traps

 Assertions/ invariants – always/ never properties  The model specifies only the allowed behaviours

Åbo Akademi University, Dec 2011

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Add Test Purpose

s2 s3 e6: i6/ o6, t 6= true e7: i7/ o7, t 7= true s1 e0: i0/ o0 t 0= true e1: i0/ o1, t 1= true e2: i2/ o2, t 2= true e3: i3/ o3, t 3= true e4: i3/ o4, t 4= true e5: i5/ o5, t 5= true bool t 0 = false; ... bool t 7 = false;

J.Vain “...Reactive Planning Testing...”Abo, Feb 3, 2012 28

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J.Vain “...Reactive Planning Testing...”Abo, Feb 3, 2012

Example: Adding the Test Goal

s2 s3 e6: i6/o6, t6=true e7: i7/o7, t7=true s1 s1 e0: i0/o0 t0=true e1: i0/o1, t1=true e2: i2/o2, t2=true e3: i3/o3, t3=true e4: i3/o4, t4=true e5: i5/o5, t5=true Initial values: bool t0 = false; ... bool t7 = false;

Trap variables are initially set to false Trap update functions are executed (set to true) when the transition is visited Test goal is defined by trap variables ti attached to transitions

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Model of the tester

 Generated from the SUT model decorated with test purpose  Transition guards encode the rules of online planning  2 types of tester states:

 active – tester controls the next move  passive – SUT controls the next move

 2 types of transitions:

 Observable – source state is a passive state (guard ≡ true),  Controllable – source state is an active state (guard ≡ pS / \ pT

where pS – guard of the SUT transition; pT – gain guard)

The gain guard (defined on trap variables) must ensure that only the outgoing edges with maximum gain are enabled in the given state.

J.Vain “...Reactive Planning Testing...”Abo, Feb 3, 2012 30

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Construction of the Tester Sceleton

s2 s3

e6: i6/ o6, t 6= true e7: i7/ o7, t 7= true

s1

e0: i0/ o0 t 0= true e1: i0/ o1, t 1= true e2: i2/ o2, t 2= true e3: i3/ o3, t 3= true e4: i3/ o4, t 4= true e5: i5/ o5, t 5= true

s1 sa s2 sb sc s3 sd se sf

J.Vain “...Reactive Planning Testing...”Abo, Feb 3, 2012

States: Transitions:

  • active
  • observable
  • passive
  • controllable

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Add IO and Gain Guards

s1 s4 s2 s5 s6 s3 s6 s9 s7

eo

0:

  • 0/ t 0= true

eo

1:

  • 1/ t 1= true

ec

01:

[ pc

01(T)]

  • / i0

eo

2:

  • 2/ t 2= true

eo

7:

  • 7/ t 7= true

ec

2:

[ pc

2(T)]

  • / i2

ec

34:

[ pc

34(T)]

  • / i3

eo

3:

  • 3/ t 3= true

eo

6:

  • 6/ t 6= true

ec

6:

[ pc

6(T)]

  • / i6

ec

7:

[ pc

7(T)]

  • / i7

ec

5:

[ pc

5(T)]

  • / i5

eo

5:

  • 5/ t 5= true

J.Vain “...Reactive Planning Testing...”Abo, Feb 3, 2012 32

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Constructing the gain guards (GG): intuition

 GG must guarantee that

 each transition enabled by GG is a prefix of some locally

  • ptimal (w.r.t. test purpose) path;

 tester should terminate after the test goal is reached or

all unvisited traps are unreachable from the current state;

 to have a quantitative measure of the gain of executing

any transition e we define a gain function ge that returns a distance weighted sum of unsatisfied traps that are reachable along e.

J.Vain “...Reactive Planning Testing...”Abo, Feb 3, 2012 33

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Constructing Gain Guards: intuition

J.Vain “...Reactive Planning Testing...”Abo, Feb 3, 2012

Observed current state Alternative choices ... tri trj trk ... Planning cones to be covered for decision making

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Constructing Gain Guards: intuition

J.Vain “...Reactive Planning Testing...”Abo, Feb 3, 2012

tri trj trk Alternative continuation choices ... ge(tri,T ) ... ge(trj,T ) ge(trj,T ) Gain functions characterize planning

  • ptions

35

Current state

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Constructing the gain guards: properties of gain functions

 ge = 0, if it is useless to fire the transition e from the

current state with the current variable bindings;

 ge > 0, if fireing the transition e from the current state with

the current variable bindings visits or leads closer to at least one unvisited trap;

 gei > gej for transitions ei and ej with the same source state,

if taking the transition ei leads to unvisited traps with smaller distance than taking the transition ej;

 Having gain function ge with given properties define GG:

pT ≡ (ge = maxk gek) ∧ ge > 0

J.Vain “...Reactive Planning Testing...”Abo, Feb 3, 2012 36

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Constructing the Gain Functions: shortest path trees

 Reachability problem of trap labelled transitions can

be reduced to single-source shortest path problem 1.

 Arguments of the gain function ge are then

 the shortest path tree TRe with root node e  VT – vector of trap variables

 To construct TRe we create a dual graph G = (VD,ED)

  • f the tester control graph MT where

 the vertices VD of G correspond to the transitions of the MT,  the edges ED of G represent the pairs of consequtive

transitions sharing a state in MT (2-switches)

1 - Fredman & Tarjan 1987

O(E + V log V)

J.Vain “...Reactive Planning Testing...”Abo, Feb 3, 2012 37

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Constructing the Gain Guards: shortest path tree (example)

The dual graph of the tester model The shortest-paths tree (left) and the reduced shortest-paths tree (right) from the transition ec

01

J.Vain “...Reactive Planning Testing...”Abo, Feb 3, 2012 38

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Constructing the gain guards: gain function (1)

Represent the reduced tree TR(ei, G) as a set of elementary sub-trees each specified by the production νi ←j∈{ 1,..n} νj

Rewrite the right-hand sides of the productions as arithmetic terms: (3)

t↑i - trap variable t i lifted to type N,

c - constant for rescaling the numerical value of the gain function,

d(ν0, νi) the distance between vertices ν0 and νi, where l - the number of hyper-edges on the path between ν0 and νi w j – weight of j-th hyperedge

J.Vain “...Reactive Planning Testing...”Abo, Feb 3, 2012 39

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Constructing the gain guards: gain function (2)

 For each symbol νi denoting a leaf vertex in

TR(e,G) define a production rule (4)

 Apply the production rules (3) and (4) starting

from the root symbol ν0 of TR(e,G) until all non- terminal symbols νi are substituted with the terms that include only terminal symbols t↑i and

d(ν0, νi)

J.Vain “...Reactive Planning Testing...”Abo, Feb 3, 2012 40

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Example: Gain Functions

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Example: Gain Guards

J.Vain “...Reactive Planning Testing...”Abo, Feb 3, 2012 42

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J.Vain “...Reactive Planning Testing...”Abo, Feb 3, 2012

Example: Construction of the Tester Structure

s2 s3

e6: i6/ o6, t 6= true e7: i7/ o7, t 7= true

s1

e0: i0/ o0 t 0= true e1: i0/ o1, t 1= true e2: i2/ o2, t 2= true e3: i3/ o3, t 3= true e4: i3/ o4, t 4= true e5: i5/ o5, t 5= true

s1 s4 s2 s5 s6 s3 s6 s9 s7

eo

0:

  • 0/ t 0= true

eo

1:

  • 1/ t 1= true

ec

01:

[ pc

01(T)]

  • / i0

eo

2:

  • 2/ t 2= true

eo

7:

  • 7/ t 7= true

ec

2:

[ pc

2(T)]

  • / i2

ec

34:

[ pc

34(T)]

  • / i3

eo

3:

  • 3/ t 3= true

eo

6:

  • 6/ t 6= true

ec

6:

[ pc

6(T)]

  • / i6

ec

7:

[ pc

7(T)]

  • / i7

ec

5:

[ pc

5(T)]

  • / i5

eo

5:

  • 5/ t 5= true

eo

4:

  • 4/ t 4= true

The gain guards guarantee that only the outgoing edges with maximum gain are enabled in the given state

43

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Complexity of constructing and running the tester

The complexity of the synthesis of the reactive planning tester is determined by the complexity of constructing the gain functions.

For each gain function the cost of finding the TRe by breadth-first- search is O(| VD| + | ED| ) [ Cormen] , where

| VD| = | ET| - number of transitions of MT

| ED| - number of transition pairs of MT (is bounded by | ES| 2)

For all controllable transitions of the MT the upper bound of the complexity of the computations of the gain functions is O(| ES| 3).

At runtime each choice by the tester takes O(| ES| 2) arithmetic

  • perations to evaluate the gain functions

J.Vain “...Reactive Planning Testing...”Abo, Feb 3, 2012 44

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Performance of reactive planning

J.Vain “...Reactive Planning Testing...”Abo, Feb 3, 2012

  • Test Goal: All Transitions
  • Test Goal: Selected Transition

45

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SLIDE 44

J.Vain “...Reactive Planning Testing...”Abo, Feb 3, 2012

Experiments: Case Study: Model of the IUT

Model of Feeder Box Controller power management (31 states, 73 transitions)

46

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How to plan in large models?

Test goal: all transitions

100 1000 10000 100000 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 horizon steps Planning with horizon + anti-ant Planning with horizon + random choice

10 100 1000 10000 0 1 2 3 4 5 6 7 8 9 10 1112 13 1415 16 1718 19 20 horizon steps Planning with horizon + anti-ant Planning with horizon + random choice

Test goal: single transition

J.Vain “...Reactive Planning Testing...”Abo, Feb 3, 2012 48

Average lengths of the test sequences

  • Adjustable planning horizon
  • Dependancy between horizon and test length

Horizon saturation point

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Shaping RPT planning cones (i)

J.Vain “...Reactive Planning Testing...”Abo, Feb 3, 2012

tri trj trk Decision point Alternative choices In online-testing decision time must be strictly bounded! ... ge(tri,T ) ... ge(trj,T ) ge(trj,T ) Gain functions

49

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Shaping RPT planning cones (ii)

J.Vain “...Reactive Planning Testing...”Abo, Feb 3, 2012

tri trj trk Decision point Alternative choices Decision time strictly bounded! Prune the cone! ... ge(tri,T ) ... ge(trj,T ) ge(trj,T ) Gain functions Horizon h Different ways for defining h, and ‘visibility’ depending

  • n depth of tree.

Fully visible Partially visible

50

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Average lengths of test sequences in the experiments

J.Vain “...Reactive Planning Testing...”Abo, Feb 3, 2012 Horizon All transitions test coverage Single transition test coverage Planning with horizon Planning with horizon anti-ant random choice anti-ant random choice 18345 ± 5311 44595 ± 19550 2199 ± 991 4928 ± 4455 1 18417 ± 4003 19725 ± 7017 2156 ± 1154 6656 ± 5447 2 5120 ± 1678 4935 ± 1875 1276 ± 531 2516 ± 2263 3 4187 ± 978 3610 ± 2538 746 ± 503 1632 ± 1745 4 2504 ± 815 2077 ± 552 821 ± 421 1617 ± 1442 5 2261 ± 612 1276 ± 426 319 ± 233 618 ± 512 6 2288 ± 491 1172 ± 387 182 ± 116 272 ± 188 7 1374 ± 346 762 ± 177 139 ± 74 147 ± 125 8 851 ± 304 548 ± 165 112 ± 75 171 ± 114 9 701 ± 240 395 ± 86 72 ± 25 119 ± 129 10 406 ± 102 329 ± 57 73 ± 29 146 ± 194 11 337 ± 72 311 ± 58 79 ± 30 86 ± 59 12 323 ± 61 284 ± 38 41 ± 15 74 ± 51 13 326 ± 64 298 ± 44 34 ± 8 48 ± 31 14 335 ± 64 295 ± 40 34 ± 9 40 ± 23 15 324 ± 59 295 ± 42 25 ± 4 26 ± 5 16 332 ± 51 291 ± 52 23 ± 2 24 ± 3 17 324 ± 59 284 ± 32 22 ± 2 21 ± 1 18 326 ± 66 307 ± 47 21 ± 1 21 ± 1 19 319 ± 55 287 ± 29 21 ± 1 21 ± 1 20 319 ± 68 305 ± 43 21 ± 1 21 ± 1 51

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SLIDE 49

Average time spent for online planning of the next step

J.Vain “...Reactive Planning Testing...”Abo, Feb 3, 2012

13 14 15 16 17 18 19 20 21 22 23 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 horizon msec All transitions Single transition

52

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SLIDE 50

J.Vain “...Reactive Planning Testing...”Abo, Feb 3, 2012 53

How to generate test data?

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SLIDE 51

Example: RPT synthesis for INRES protocol

J.Vain “...Reactive Planning Testing...”Abo, Feb 3, 2012 54

Model of the SUT local component

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SLIDE 52

Example: RPT synthesis for INRES protocol

Local SUT component model RPT model

Timeout! Off! LowICONreq? DR? CC?

?

AK?

55 J.Vain “...Reactive Planning Testing...”Abo, Feb 3, 2012

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SLIDE 53

Online planning constraints for the RPT

J.Vain “...Reactive Planning Testing...”Abo, Feb 3, 2012 56

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SLIDE 54

How to derive data constraints?

 For all transitions t(si,.) of state si generate reduced

reachability tree RRTi s.t.

 transition t(si,.) is a root and the trap labeled transitions the

terminal nodes of the RRTi .

 Compute data constraint for each path πj of RRTi

 use wp-algorithm (starting from trap node) for pairs of neigbbour

traps of πj

 unfold loops using gfp for termination  for constructing the gain function of πj record (when traversing πj):

  • traps remaining on the path πj and
  • the lengths of inter-trap paths

 construct the gain function for full path πj using trap-to-trap

distances on that path and the vector of trap variables.

 Global data constraint for the path is a conjunction of data

constraints pairwise traps of πj

57 J.Vain “...Reactive Planning Testing...”Abo, Feb 3, 2012

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SLIDE 55

Summary

 RPT synthesis technique relies on offline static

analysis of the SUT model and test goals.

 Efficiency of planning:

 Number of rules that have to be evaluated at each step is

relatively small (i.e., = the number of outgoing transitions

  • f a current state)

 The execution of decision rules is significantly faster than

looking through all potential alternatives at runtime.

 Scalability supported by RRT pruning technique  Provides test sequences that are lengthwise close to

  • ptimal.

J.Vain “...Reactive Planning Testing...”Abo, Feb 3, 2012 58

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SLIDE 56

DTRON: Distributed RPT execution

(http://dijkstra.cs.ttu.ee/~aivo/dtron/usage.html)

59

SPREAD

J.Vain “...Reactive Planning Testing...”Abo, Feb 3, 2012

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SLIDE 57

||

LTC communicating timed automata (TA)

60

TA model of local SUT component (extended with online planning constraints) TA model

  • f the local

test goal (property)

Ms Mg ||

TA model of local SUT component (extended with online planning constraints) TA model

  • f the local

test goal (property)

Ms Mg “TCI update” signals “Test coverage item (TCI) reached” signal TCI synchro LTC LTC

J.Vain “...Reactive Planning Testing...”Abo, Feb 3, 2012

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SLIDE 58

61

Tester components communicate over channels

J.Vain “...Reactive Planning Testing...”Abo, Feb 3, 2012

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DTRON: distibuted, heterogeneous components

62

SPREAD Selenium TestCast Z3

J.Vain “...Reactive Planning Testing...”Abo, Feb 3, 2012

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SLIDE 60

DTRON: dynamic test reconfiguration

63

SPREAD Selenium TestCast Selenium Model (Testerk ) Model (Tester1) Model (Test Plan Supervisor) Tester component updates

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SLIDE 61

DTRON: Visualization of test runs

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SLIDE 62

Questions?

Thank You!

J.Vain “...Reactive Planning Testing...”Abo, Feb 3, 2012 65