Adapting Simulation Modeling to Model-Driven Architecture
for Model Requirement Verification
Fuqi Song, Gregory Zacharewicz and David Chen
Univ. Bordeaux, IMS, UMR 5218, F-33400 Talence, France
Keywords: Model-Driven Architecture, Model Verification, Ontology, DEVS, Simulation Interoperability.
Abstract: In order to automate the software development process and enhance interoperability and reusability of it,
Model-Driven Architecture (MDA) was proposed aiming to achieving these goals. MDA mainly involves
creating models and performing model transformation, it is a high model-intensive process. However, how
to verify the models by respecting to the requirements becomes a concern in the automation process. In this
paper, we describe the work of proposing a method using ontology as information exchange media to adapt
simulation modeling to MDA towards this goal. Simulation is integrated loosely to MDA for verifying the
models with the pre-defined requirements to check if expected goals are fulfilled. An illustrative case study
is given to explain visually the method.
1 INTRODUCTION
Model-Driven Architecture (MDA) was proposed to
solve some issues in traditional software
development, such as, interoperability and
reusability on different platforms, efficiency of
software development. In MDA, the models are
created in different levels concerning different
aspects of software development. Between different
levels of models, model transformation is applied to
convert model from one to another. It aims to
automate the whole process and generate the final
codes and applications. The whole process of
creating models and transforming models is high
model-intensive. In the process, there is no
verification and validation of the models by
respecting the requirements. We consider that
simulation could aid to verify the models by
executing models in practice.
How to verify the models before the software is
developed? An intuitive idea is to simulate the
models before they are used for further steps. If the
issues are identified in early stage of development,
the models could be improved and save costs by
avoiding the application modifications in future.
However, not all the models created in MDA can be
simulated. Generally, the models are classified as
dynamic and static models. The major difference is
that dynamic models describe the abstraction of
systems at run-time, whereas static models capture
the unchanged features regardless at run-time or not.
The scope of the verification is among dynamic
models, such as, activity diagrams and state chart.
The static models, such as, class diagram and object
diagram, are beyond of the scope.
In order to carry out simulation, model creation
is the prerequisite. The proposal of this paper is to
adapt models in MDA to simulation modeling
process. It is different from model transformation
from one model to simulation model. The adaptation
is a loose connection rather than tight mapping
relation. The media to exchange information
between MDA models and simulation is ontology.
Ontology is a formal representation to conceptualize
concepts and the relations among them. The data of
MDA models is described by a specific ontology,
and the ontology is transformed to the other side to
aid the creations of simulation models. Also,
expected results or goals are described in this
ontology for verifying the model. Another specific
ontology with verification criterion and results will
be generated and sent back to MDA models for
potential improvements.
Ontology is brought to simulation in recent
research. As stated by Turnitsa et al. (2010), two of
the roles of ontology in simulation are: ontology as
specification and common access to information. In
this work, ontology has the two uses in facilitating
interoperability between MDA model and simulation.
302
Song F., Zacharewicz G. and Chen D..
Adapting Simulation Modeling to Model-Driven Architecture for Model Requirement Verification.
DOI: 10.5220/0004585603020309
In Proceedings of the 3rd International Conference on Simulation and Modeling Methodologies, Technologies and Applications (SIMULTECH-2013),
pages 302-309
ISBN: 978-989-8565-69-3
Copyright
c
2013 SCITEPRESS (Science and Technology Publications, Lda.)
The rest of the paper is organized as follows.
Section 2 recalls MDA and simulation modeling,
also analyzes the links between MDA and
simulation. Section 3 describes the proposed method
with ontology to adapt simulation to MDA for
aiding the requirements verification. Section 4
demonstrates a case study to illustrate the proposed
method. Section 5 draws some major conclusions
and extends the future work.
2 MDA AND SIMULATION
MODELING
2.1 Model-Driven Architecture (MDA)
The model for developing computer systems and
applications could be represented concisely in
Figure 1. In real world, there are many issues and
work need to be solved and done, and the goal is to
construct computer systems to do them, namely, to
find computational solutions to the problems in real
world.
Figure 1: Relation of real world and computer system.
The process of developing applications has been
transformed from procedural towards component-
based years ago. However, some major issues still
exist in software development, such as platform
portability, interoperability issues and productivity
problems. MDA was proposed by Object
Management Group (OMG) on 2001 to improve the
process and overcome the weakness in the other
approaches. The main goal of MDA is to facilitate
portability, interoperability and reusability of
systems (OMG, 2003). Model-Driven Engineering
(MDE) and model transformation are the bases and
relevant research areas to MDA. MDE technologies
provides promising methods to solve the inability of
third-generation languages to alleviate the platforms
complexity and express domain concepts effectively
(Schmidt, 2006). Model transformation enables the
conversion between models from high abstract level
to low abstract level, ultimately to implementation.
Kuster (2006) formalizes the model transformation
with mathematics and define a set of criteria to
validate the transformation. Czarnecki and Helsen
(2006) give a feature-based survey of the approaches.
In MDA, three levels of models are defined by
focusing on certain aspects in software development
life cycle as shown in Figure 2. The top level is
Computation Independent Model (CIM), which is
independent to the future systems and focuses on
only business requirements and models. The next
level is Platform Independent Model (PIM). PIM
describes the requirements of software system
regardless specific platform going to be used. Last
level is Platform Specific Model (PSM), which
concerns the models to be used on a specific
platform, for example, oracle database schema and
java beans. MDE and model transformation
automate the process of conversion from abstraction
to implementation.
Figure 2: Illustration of MDA.
2.2 Simulation Modeling
Simulation is the process of simulating real world in
order to observe and verify the real world from
certain perspectives. Before performing simulation,
the models, which the simulation follows, should be
created. Thus there is another step involved:
modeling. In the book of theory of simulation and
modeling written by Zeigler (1976), a generic
relation among real world, modeling and simulation
is given as Figure 3.
Figure 3: Basic elements and relation of modeling and
simulation (Zeigler, 1976).
By focusing on certain aspects of real world, the
reality is abstracted and created into models, namely,
models are the abstraction of real world from a
specific perspective. Simulation is performed by
following the models on computer systems with
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simulation languages. Pollacia (1989) gives a review
of discrete event simulation languages. The
objective of simulation is to observe the behaviors of
one system, in order to understand the reality and
predict the behaviors in future. It is widely applied
in many areas.
2.3 Connections between MDA
and Simulation Modeling
Bourey and Seguer (2011) describes that a model is
a consensus about an abstraction of phenomena of
the real world, in other word, a representation of an
aspect of the world for a specific purpose. From this
definition, we can see that when talking about
models, the objectives and scope should always be
stated, otherwise pure models are not helpful. In
general, the models in MDA and models for
simulation are different, they emphasize on different
aspects. Models in MDA try to understand the
reality in real world or future systems by exploring
certain aspects,for instance, using BPMN to analyze
the business process, using E-R diagram to analyze
the data structure, or using state chart to study the
changes among different states. However, the
models for simulation focus more on the execution,
the models need to be executable. Major elements
for simulation are event, time and behaviors. Thus
with the aides of model transformation and extra
information, the models created in MDA can be
executed for simulation.
In MDA, creating models and transforming
models are two major activities involved, namely,
MDA is a model-intensive process. Models are
staying at an abstract level. An existing issue is how
to verify whether these models are good. A general
standard is that M is a good model of S if M makes it
possible to answer predefined questions about S in a
satisfactory way (Ross). This standard is quite blur
and hard to formolize. But we can see that a good
model should respect the facts as one of the main
objectives of simulation. Requirement verificatioin
could be a strong complement to MDA. The purpose
of this paper is to introduce simulation to MDA for
model verification with respecting to the
requirements at a high level.
3 ADAPTING SIMULATION
MODELING TO MDA
In this section, we describe how to apply simulation
with MDA. First, a general structure to combine
simulation with MDA is illustrated, and then the
specific ontology for information exchange is
proposed.
3.1 General Structure
According to the structures of MDA and simulation
and the connections between them discussed in
previous section, first a general structure for
adapting simulation to MDA is illustrated in Figure
4. CIM, PIM and PSM belongs to modeling process,
they will create different models. A simulation
process will be used to verify the models by
respecting the real requirements. An iterative
process between modeling in MDA and simulation
is constructed to involve simulation in MDA. The
information concerning event and behaviors will be
sent to the other side to perform simulation process.
The simulation results will be compared with
expected results and generate a verification report.
This report is used to improve the modeling process
in MDA. If necessary, several iterative processes
will be carried out.
Figure 4: Combination of MDA and simulation.
Figure 5 demonstrates an elaborated structure with
exchange information. The formalism of information
exchange and sharing between the two sides is
represented by ontology. Ontology of Information
Exchange (OoIE) describes the models in MDA and
is sent to the other side for aiding to create
simulation models.
Figure 5: Structure of adapting simulation to MDA.
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Besides the ontology OoIE, in order to create
simulation models, additional information is
required, such as time constraints and sequential
limitations. The information will be added when
creating the simulation models. Also, the expected
results of models in MDA are described in OoIE, in
order to verify and validate whether the built models
correspond to the requirements.
3.2 Ontology of Information Exchange
(OoIE)
Ontology represents a set of concepts and relations
between them in a specific domain of knowledge.
Ontology has several advantages in facilitating
interoperability of models and exchange information,
especially, the semantic representation in term of
knowledge sharing and exchange. It represents the
semantics explicitly, which can be processed and
understood by computers. In (Song et al., 2012a),
the authors classify three roles that ontology played
in contributing to interoperability of enterprise
modeling: concepts specification, knowledge sharing,
and concepts annotation. As a type of media for
knowledge sharing, ontology plays an effective role.
There are various languages to represent ontology,
which can be chosen depending on particular
demands. An investigation of ontology languages is
given in (Song et al., 2013).
At the different levels of MDA process, the
models are created to represent certain aspects of
requirements. Each model is built in a specific
modeling language, such as using BPMN to gather
and analyze the business requirements at CIM level,
using activity diagram to model the main functions
involved in the process at PIM level. OoIE aims to
build a semantic representation at a generic level to
describe the main concerns of the models. The
ontology will be sent to the simulation side and aid
to create simulation models. The ontology describes
mainly three parts: model itself, description and
mappings as shown in Figure 6.
Figure 6: Ontology of Information Exchange (OoIE).
The models are the ones from MDA, different
models will be formalized and represented with
ontology in the form of meta model. The
requirements for creating the models will be added
to OoIE. The functions are twofold: 1) aiding to
create simulation models, 2) verifying the
consistency between requirements and built models.
Mappings are for alignment between MDA models
and simulation models.
The purpose to build mappings between MDA
model and simulation model is to facilitate the
creation and transformation of simulation models.
The mapping is carried out based on the ontology of
models, thus a hypothesis is that the description
ontology of MDA models and simulation models are
existing. The description ontology is a semantic
representation of models and reality. To find the
equivalent concepts between the source ontology
and target ontology, several levels from different
aspects are investigated. The mappings are set up by
measuring the similarity between different elements
in ontology, and the measurement is based on
different algorithms and rules. Song et al. (2012b)
present a multiple strategies-based ontology
matching approach. In this approach, several
matching algorithms are proposed from different
levels of ontology, and the different matching results
are combined by an analytic process AHP by
balancing the weights of each matcher. After having
obtained the mappings between equivalent concepts,
the creation of simulation models would be
facilitated by understanding the connections between
the models.
3.3 Models Adaptation
With the help of OoIE, a general structure of model
adaptation is illustrated in Figure 7. The whole
process is started from the models in MDA, and only
part of the models is taken into account. Because of
the natures of models, it is argued that only dynamic
models in MDA could be adapted to simulation
models. The major difference between dynamic and
static models is whether the model captures
the behaviors in system run times or not. Normally,
static models do not include the elements of
Figure 7: Model adaptation between MDA and simulation.
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behaviors and time constraints, so we consider that
this kind of models is beyond of the scope.
OoIE describes the models in MDA, in order to
aid to create simulation models, some additional
information is needed. OoIE mainly describes the
data and behaviors occurred in MDA, however, to
adapt to specific simulation models, constraints and
parameters should be complemented, such as time
limitation and sequential requirements. This process
is different from model transformation. Model
transformation is a strict mapping process to
transform elements from source models to target
models. The whole process is supposed to be
automatic. However, in this process, the model
adaptation requires a loose mapping between MDA
models and simulation models, and the process is
manual work and adjustment involved.
Simulation models will generate simulation
results after execution. In order to verify if the
models are executed as expected, the generated
results are compared with expected results, which
are described in OoIE. The results are in two forms,
first form is the general execution results of models,
such as the specific behavior generated or state
arrived. For some models and situations, they cannot
generate general results as all the models do; the
results vary depending on different scenario. In this
case, a specific case or scenario and expected results
are described in OoIE.
The results of verification are sent back to the
MDA side. The differences where the goals are not
reached will also be noted, so that to improve the
models. If the validation results do not reach to a
satisfactory level, more iterative processes will be
done.
4 DEMONSTRATION
A study case is made to illustrate the proposed idea
and method. In this study case, first the scenario in
the domain of manufacture and background of case
are described. At PIM level in MDA, a state chart is
created according to the requirements. And then in
order to verify the modeled state chart, we follow
the methodology described in above sections to
verify and improve the built model.
4.1 Scenario
In product manufacturing, order processing is an
ordinary requirement. In MDA, after requirements
gathering at CIM level, the client want to focus on
the state change of order processing at PIM level
using state chart. The requirements are described as
follows:
The state of order changes from
“
initial” to
“Ordered” after selection products. And then the
system check the stock, if the product is out of
stock, then the order will be cancelled and the
order process is completed. If the product is
available, then the order awaits the payment from
user. After user pays the order, the manager will
verify (manually or automatically) the payment. If
the payment is received correctly, then the order
is confirmed and the stock prepares to deliver the
product. If the payment is not received correctly,
then the order changes to state “awaiting
payment”. The user could cancel the order after
ordering the products and before the products are
delivered.
An existing data model of orders is given in form of
ontology in Figure 8. According to the description of
scenario, the model is created in OWL, which is a
standard ontology language and is used mostly to
describe models in MDA. This model illustrates the
knowledge of order state changing.
Figure 8: Data model of order states in OWL.
A version of state chart is created according to above
description as shown in Figure 9. Now the demand
to check whether the model corresponds to the
requirements is required.
Figure 9: Modelled state chart of order processing.
4.2 Model Adaptation
In this section, first a meta model of state chart is
described, and then the mappings between two
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models in different representation languages are
represented. The discovered concepts are used in
simulation models.
4.2.1 Model Data
The description of state chart meta data can be in
different ways, Favre (2010) describes a metadata
model of state chart. A simplified ontology is used
to describe the state chart as shown in Figure 10.
The model starts with an initial state and ends with
an end state, between them, there are iterative states
with transitions.
The possible sequences of state transition derived
from model in Figure 9 are:
1) Initial- Ordered-out of stock- canceled-
completed;
2) Initial- Ordered- Awaiting payment- paid-
confirmed- delivered- completed.
Figure 10: Description of simplified state chart.
4.2.2 Mappings
Figure 11 shows a data model represented in System
Entity Structure (SES), it is an existing model used
in simulation side. SES is presented by Zeigler and
Hammonds (2007), which plays roles as ontology
framework and information exchange media, to
describe static and dynamic work state change in
simulation modeling with DEVS formalism.
Figure 11: Data model of order state described in SES.
The second step is to find mappings between the
model in SES (see Figure 11) and the model in
OWL (see Figure 8). This step enables to find
correspondent concepts between two data models.
The matching method used is from Song et al.
(2012b). The left part is in format of SES and right
part is in OWL, and discovered alignments are
labeled in dotted line with a value of similarity. The
similarity measures the sameness of two concepts in
term of semantic. For example, the similarity
between “pending” and “Awaiting” is 0.6, and
“paidOrder” and “paid” is 0.9. The discovered
correspondences are also listed in Table 1. The
threshold of similarity is set as th = 0.5 manually, for
these correspondences, whose similarity value is
greater than 0.5, they will be used. The equivalent
concepts will be applied in simulation models.
Figure 12: Alignment of two data models.
Table 1: Discovered correspondences.
Model in OWL Model in SES Sim.
order order/orders 1.0
client customer 0.8
approved confirmed 0.7
paid paidOrder 0.9
awaiting pending 0.6
canceled canceled 1.0
-
delivered/
orderSpec/orderDec
-
state/commands/
contains
has/instantiate
- -
4.3 Verification and Improvements
Simulation tools and languages can help to facilitate
this step, such as, Wood et al. (2008) uses VHDL to
represent UML state diagram. At current stage, we
simulate the process using LSIS_DME (Baati et al.,
2007) that proposes to create G-DEVS graphical
models. The snapshot is shown as Figure13. The
generated DEVS model needs to be defined by
temporal time life, for instance, the state time life is
represented in the DEVS simulation model.
According to the metadata of state chart in
Figure 10 and requirement description, the sequence
of real state changes are as follows, where [xxx]*
refers to a loop:
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1) Initial-Ordered -canceled-completed
2) Initial- Ordered- [pending- paidOrder]*-
canceled- completed
3) Initial- Ordered-[pending- paidOrder]*-
confirmed- canceled- completed
4) Initial-Ordered- [pending- paidOrder]*-
confirmed- delivered- completed
Figure 13: Snapshot of simulation model with LSIS_DME.
Compared with the sequences, which are described
in the first version of modeled state chart (see Figure
9), an improved model is given in Figure 14. The
dotted line refers to the added entities. The
improvements are as follows:
1) Remove the state “out of stock”;
2) Add state “delivered”;
3) Add transition between “paidOrder” and
“Pending”;
4) Add transition between “paidOrder” and
“canceled”;
5) Add transition between “confirmed” and
“canceled”.
Figure 14: Improved state chart of order processing.
4.4 Discussion
There is little work about combining MDA and
simulation modeling. In (El Haouzi, 2006), the
author proposes a method combining MDA and
HLA (High Level Architecture) to improve the
deficiencies of current simulation methods at the
level of interoperability and reusability. Cortellessa
et al. (2007) extends MDA to non-functional-MDA
framework to allow generating non functional
models. The extended framework allows verifying
the requirements of models. The work described in
this paper differs from the above two methods. The
proposal advocates running the models and then
getting the real feedback by comparing the results
with expected requirements. The work proposes a
generic methodology of combination MDA and
simulation modeling process. In the process of
adaption, ontology plays a key role as the media of
information exchange.
Benjamin et al. (2007, 2009) applies ontology as
information exchange media to enable integration
and interoperability of simulation models. In the
approach, three kinds of ontology: domain ontology,
Community Of Interest (COI) ontology and
simulation tool ontology are extracted and created.
COI ontology builds a bridge of common concepts
and knowledge between simulation models and
domain knowledge, ultimately to enable the
communication and interoperability of models.
Ontology is also adopted as media of information
exchange in the approach of this paper. However, its
purpose is to enable the knowledge sharing between
MDA and simulation modeling. Also, ontology is
applied as simulation model to enhance semantic in
simulation, for instance, Silver et al. (2011) proposes
Discrete-event Modeling Ontology (DeMO) to
improve semantic search and integration of
heterogeneous information sources.
5 CONCLUSIONS AND FUTURE
WORK
In this paper, we described a generic methodology to
adapt simulation modeling to MDA for verifying the
models by respecting to the contextual requirements.
It aims to complement and enrich the MDA process.
Combination of MDA and simulation can contribute
to the model verification at requirement level. The
application of ontology as information exchange and
sharing media provides a semantic and loose relation
between models and simulation.
At current stage, the work is ongoing, and the
work that has been done focusing on defining a
general methodology and framework, also giving a
typical illustrative demonstration. Remaining work
mainly concerns elaborating the method and giving
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operational application steps, also enabling the
automatic process. First, the rules and formalism of
OoIE will be defined. This specific ontology can
vary enormously depending on different models, so
different kinds of ontology concerning different
models will be defined. Second, the way of
exchanging information between the two sides needs
to be elaborated by defining the interface and format
in details. Third, the way and criterion of
verification will be elaborated in order to enable
automatic verification.
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