ETTC'2003 - SEE

predict performance in an incorrect situation or environment. Therefore a mutual trust
between simulation and tests is fundamental.
VV&A and VV&C
The definitions given in the following paragraphs are taken from the M&S Master
Plan, published by the US DoD in October 1995, and from the NATO M&S Master Plan
AC/323(SGMS)D/2, published in August 1998. They are widely accepted by the international
simulation community.
An efficient evaluation strategy should include a verification and validation (V&V) of
models and simulation, following the increasing maturity of the system, in order to establish
credible information.
Following these definition, verification is the process that determines whether a model
or a simulation represents precisely and faithfully the conceptual description and the
development specification. This process includes also a verification of the software
engineering techniques used. To make a long story short, the aim is to prove that what has
been done corresponds to what was required and that is was done correctly.
Validation is the process that determines the adequacy of the model or the simulation
to the real-world from the point of view of the intended use of the M&S. In other words, the
issue is to evaluate the way in which a response was brought to the initial problem.
Based on an adequate V&B, the model or the simulation is determined as acceptable
for an intended use within a specific application framework. This constitutes the accreditation.
It should be noticed that all data used by M&S have to certified also by a process called
VV&C (verification, validation and certification). Supplementary definitions follow.
Data verification, from the data producer point of view, consists in the use of
techniques and procedures that guarantee that the data satisfy constraints defined by the data
standards. From the data user point of view, verification consists in the use of techniques and
procedures that guarantee that the data follow standards and usual rules.
Data validation consists in the documented evaluation by domain experts and by
comparison with common reference values. From the data producer point of view, this
evaluation has to be done following explicit criteria and assumptions. From the user point of
view, the evaluation is done with respect to the intended use within a particular model.
The VV&C process checks the internal consistency and the correction of the data,
validates the fact that they represent entities of the real-world in conformity with the intended
use, and certifies that the data have the specified quality level that corresponds to the intended
use. Here also, this process has two perspectives as before, depending on the data producer
and the data user.
Another problem is that data can be available under two forms. They can be
unprocessed, coming from tests, available literature, intelligence…, and their credibility
depends then on the collection process and the intrinsic credibility of the used sources of
information. But then also be aggregated, i.e. generated by the use of various processing on
unprocessed data. It is then necessary to have a closer examination of the adequacy of the data
with the models and their intended use.
The main points to remember is that data coming from laboratory or field tests will be
integrated in order to validate models and simulations, as the system matures. This leads to a
complete set of M&S which gains fidelity (this is the verification aspect) and credibility (this
is the validation aspect). It is essential to remember that any validation is done with an
intended use in mind. This has been underlined from the start on in the various simulation