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1. 1
of Simulation Computer
Program
Presented by:-
ASHISH KUMAR
Bundelkhand University
Jhansi
Presented to:-
Mr. KAMAL GUPTA

2. Verification of Simulation Computer
Program
Verification of a model is the process of
confirming that it is correctly implemented
with respect to the conceptual model.
During verification the model is tested to
find and fix errors in the implementation of
the model.
Model verification is formally defined as
“ensuring that the computer program of the
computerized model and its implementation
are correct”. 2

3. Verification of Simulation Computer
Program
Verification is like debugging—it is intended
to ensure that the model does what it is
intended to do. Models, especially simulation
models, are often large computer programs.
Verification: concerned with building the
model right. It is utilized in the comparison
of the conceptual model to the computer
representation that implements that
conception.
3

4. 4
Verification of Simulation Computer
Program

5. Verification of Simulation Computer
Program
Verification: concerned with building the
model right. It is utilized in the comparison
of the conceptual model to the computer
representation that implements that
conception.
5

6. It asks the questions:-
Is the model implemented correctly in the
computer? Are the input parameters and
logical structure of the model correctly
represented?
6

7. Many common sense suggestions can
be given for use in the verification
process.
1. Have the code checked by someone other
than the programmer.
2. Make a flow diagram which includes each
logically possible action a system can take
when an event occurs, and follow the model
logic for each action for each event type.
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8. Many common sense suggestions can
be given for use in the verification
process.
3.Closely examine the model output for
reasonableness under a variety of settings of
the input parameters. Have the code print out a
wide variety of output statistics.
4. Have the computerized model print the input
parameters at the end of the simulation, to be
sure that these parameter values have not been
changed inadvertently.
8

9. Many common sense suggestions can
be given for use in the verification
process.
5. Make the computer code as self-
documenting as possible. Give a precise
definition of every variable used, and a general
description of the purpose of each major
section of code.
9

10. Verification of Simulation Computer
Program
Verification is like debugging—it is intended
to ensure that the model does what it is
intended to do. Models, especially simulation
models, are often large computer programs.
Therefore all techniques that can help
develop, debug or maintain large computer
programs are also useful for models.
10

11. Techniques of Verification of
Simulation Computer Program
1) Anti-bugging.
2) Structured walk-through/one-step analysis.
3) Simplified models .
4) Deterministic models (simulation only).
5) Tracing (simulation only).
6) Animation (simulation only).
7) Seed independence (simulation only).
8) Continuity testing .
9) Degeneracy testing .
10) Consistency testing . 11

12. Anti-bugging.
Anti-bugging consists of including
additional checks and outputs in a model
that may be used to capture bugs if they
exist.
A common form of anti-bugging is to
maintain counters within a simulation model
which keep track of the number of entities
which are generated and terminated during
the evolution of the model.
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13. Simplified models
 It is sometimes possible to reduce the model
to its minimal possible behavior.
13

14. Deterministic models (simulation only)
For simulation models the presence of
random variables can make it hard for the
modeller to reason about the behavior of a
model and check that it is as expected or
required. Replacing random variables which
govern delays or scheduling with
deterministic values may help the modeller to
see whether the model is behaving correctly.
Note that this technique is only appropriate for
simulation models , markovian models can only be solved with
exponential distributions.
14

15. Tracing (simulation only)
Trace outputs can be extremely useful in
isolating incorrect behavior in a model.
Default tracing can be switched on and off
using the method Sim system.
15

16. Animation (simulation only)
Animation is similar to tracing but provides
the information about the internal behavior
of the model in a graphical form.
Animation can take the form of automated
one-step analysis, if the animation facilities
allow the view of the model to advance one
event at a time.
16

17. Seed independence (simulation only)
 The seeds used for random number generation in a
simulation model should not significantly a ect theff
final conclusion drawn from a model, although
there will be variation in sample points as seeds
vary. If a model produces widely varying results for
di erent seed values it indicates that there isff
something wrong within the model. Seed
independence can be verified by running the
simulation with di erent seed values, somethingff
which is probably necessary in any case.
17

18. Continuity testing
 At an abstract level all systems and models can be
thought of as generating a function from input values to
output values, and in most cases we expect that function
to be continuous.
 Continuity testing consists of running a simulation
model, or solving a Markovian model, several times for
slightly di erent values of input parameters.ff

19. Degeneracy testing
Degenerate cases for a model is those values
of input parameters which are at the
extremes of the model’s intended range of
representation.
Degeneracy testing consists of checking that
the model works for the extreme values of
system and workload parameters.
Degeneracy testing can help the modeller to
find bugs that would not otherwise have been
discovered. 19

20. Consistency testing
Consistency tests are used to check that a
model produces similar results for input
parameter values that have similar e ects.ff
20

21. Structured walk-through/one-step
analysis.
 Explaining the model to another person, or group of people, can
make the modeller focus on di erent aspects of the model andff
therefore discover problems with its current implementation.
Even if the listeners do not understand the details of the model,
or the system, the developer may become aware of bugs simply
by studying the model carefully and trying to explain how it
works. Preparing documentation for a model can have a similar
e ect by making the modeller look at the model from a di erentff ff
perspective. In the absence of a willing audience the model
developer should try to carry out the same sort of step-by-step
analysis of the model to convince himself or herself that it
behaves correctly. For a GSPN model this would amount to
playing the token game; in a queuing network, stepping through
the possible customer transitions. Some modeling packages
provide support for doing this. 21

22. Summary
i. Verification is the most important step in
any simulation study.
ii. No model is ever 100% verified.
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23. 23
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