J. Software Engineering & Applications, 2009, 2: 160-164
doi:10.4236/jsea.2009.23023 Published Online October 2009 (http://www.SciRP.org/journal/jsea)
A Solution Based on Modeling and Code Generation for
Embedded Control System
Guohua WU, Dongwu CHENG, Zhen ZHANG
School of Computer Science, Hangzhou Dianzi University, Hangzhou, China.
Received April 2nd, 2009; revised May 30th, 2009; accepted July 4th, 2009.
ABSTRACT
With the development of computer technology, embedded control system plays an important role in modern industry.
For the embedded system, traditional development methods are time-consuming and system is not easy to maintain.
Domain-specific modeling provides a solution for the problems. In this paper, we proposed development architecture
for embedded control systems based on MIC. GME is used to construct meta-model and application model, model in-
terpreter interprets model and stores model information in xml format document. The final cross-platform codes are
automatically generated by different templates and xml format document. This development method can reduce time
and cost in the lifecycle of system development.
Keywords: Domain-Specific Modeling, Mod e l Interpreter, Code Generation, Embedded Contro l System
1. Introduction
The processing capability of generous purpose micro-
processor is increasing and moving system development
emphasis from hardware to software. In order to meet the
embedded system requirements, software development
process becomes sophisticated. Developing embedded
control system with safety-critical and real-time charac-
teristics by the traditional method is time-consuming.
Matlab/Simulink [1] focuses on data visualization, al-
gorithms, analysis and numeric computing. The code can
be automatically generated from model. However, it is
not adequate for developing embedded control system.
Giotto [2] is a time-triggered language for embedded
control system which is developed by university of
Berkeley. It supports the automation of control system
designed by strictly separating platform-dependent func-
tionality from scheduling and communication. However,
developer must develop different virtual machines for
application, because application is interpreted on two
virtual machines: the scheduling and embedded machine.
In addition, the code generated by Giotto complier is
timing-code. Model-based development method is from
high level abstraction to build application. Currently,
unified modeling language (UML) [3] is the most popu-
lar modeling language. Although some diagrams are
suitable for automatic code generation, the implementa-
tion must be done by hand. As a general purpose model-
ing language, UML is unable to describe embedded con-
trol system characteristics such as deadline, and fault-
tolerance.
MIC [4–6] is a modeling framework based on models
and generation. It employs domain models to represent
system, its environment, and the relationships between
them. We use GME [7] for constructing meta-model and
application model. GME is a configurable toolkit and
supports domain-specific modeling. In the modeling
process, UML and Object Constraint Language (OCL) [8]
are used to express meta-model and constraints. The
BON2 [9] component is used to interpret model. In this
paper, we proposed an embedded control system devel-
opment framework based on domain-specific modeling.
The framework contains four layers: meta-meta model
layer, meta-model layer, model layer and implementation
layer. In the model interpreting process, we interpret
model and store information in xml format document.
The final code is generated by templates files and xml
document, implementing cross-platform codes automatic
generation.
The paper is organized as follows. In section 2, we il-
lustrate the embedded control system development ar-
chitecture. In section 3, we illustrate a meta-modeling
process and take an example of state machine in detail. In
section 4, we describe a method for model interpretation
and code generation. In section 5, we make a conclusion.

A Solution Based on Modeling and Code Generation for Embedded Control System161
2. Embedded Control System Developments
Architecture
According to the characteristics of the embedded control
system and model-integrated computing (MIC), we di-
vided model architecture into four layers (see Figure 1).
1) Meta-meta model layer. Define meta-model model-
ing language, which is a general-purpose language and
independe nt on domain.
2) Meta model layer. This is the core and infrastruc-
ture of implementing domain model.
3) Model layer. Domain developers construct applica-
tion model according to domain knowledge and relevant
rules.
4) Implementation layer. This is the concrete imple-
mentation of application model. According to tasks dis-
tribution in the model, execute software and hardware
relevant operations.
3. Construct Meta-Model for Embedded
Control System
Meta-model is expressed by UML class diagrams and
OCL expressions, which specifies the static semantic and
syntax of domain-specific language. In the process of
meta-model modeling, the characteristics that impact sys-
tem function and performance such as currency, deadline
and the worst case execut i on t ime should be specified.
Figure 1. Embedded control system model architecture

A Solution Based on Modeling and Code Generation for Embedded Control System 162
Figure 2. The meta-model modeling process
Figure 3. State machines meta-mode
3.1 Meta-Model Modeling Processes
Formalization of modeling language to be the correspo-
nding meta-model is a recursive process (see Figure 2).
1) Identifying the characteristics and properties of the
embedded control system such as real-time, safety-criti-
cal and concurrent.
2) According to the abstract principles, acquiring suit-
able model from characteristic and properties.
3) Acquiring a higher level meta-model by formalizing
model which is used to construct domain models.
4) Synthesizing domain application model by domain
modeling language. If application model can not be cor-
rect constructed, retry to acquire meta-model until it is
successful.
5) Validating the application model against system
requirement. If the application model can not meet the
system requirements, retry to abstract domain model
again.
3.2 Meta-Model of FSM
State machine is an important component in the embed-
ded control system. The state machine model (see Figure
3), including initial state, terminal state, and state transi-
tion. Event triggers state transition and each state has
sequential behaviors. Initial state and terminal state de-
note the beginning and ending of the state machines re-
spectively. State transition is a common behavior and
each state may consist of multiple transitions.
The meta-model in form of class diagram together
with the constraints expressed in OCL provides a com-
plete formal definition for model.
3.3 Embedded Control System Modeling
Language
Domain specific modeling language (DSML) employs
domain-specific concept symbols to specify restrict yet
precise semantics. Formally, a modeling language is a
five-tuple of concrete syntax (C), abstract syntax (A),
semantic domain (S), semantic mapping (Mc) and syn-
tactic mapping (Ms) [10].
L=<C, A, S, Mc, Ms>
1) Abstract syntax (A), defining the concepts, rela-
tionships, and integrity constraints available in the lan-
guage.
2) Concrete syntax (C), defining the specific (graphi-
cal or textual) notation that is used to express models. It
may be graphical, textual or mixed.
3) Semantic domain (S), which is formal semantic de-
fined by mathematical formalism in terms of the meaning
of the model is explained.
4) Semantic mapping (Ms),
A
S mapping relates
syntactic concepts to the semantic domain.
5) Syntactic mapping (Mc),
A
Cmapping syntactic
constructors (graphical, textual, or both) to the elements
of abstract syntax.
The primary participants in domain-specific modeling
are modelers. In the process of embedded control system
development, domain specific modeling language with
precise syntax and semantic will be defined from differ-
ent aspects.
4. Model Interpretation and Code Generation
Model interpreter plays an important role in the embed-

A Solution Based on Modeling and Code Generation for Embedded Control System163
ded control system development. We can develop dif-
ferent interpreters for meeting requirements. The inter-
preter is similar to a complier of advanced programming
language. We take BON2 component to interpret model
which is provided by GME. BON2 consists of class and
interface, traversing objects in the model. The final
cross-platform codes are automatic generated by differ-
ent templates.
4.1 Model Interpretation
Model interpreter is a component of GME, which is used
to acquire objects’ information in application model.
Developers can build various interpreters according re-
quirements. The interpreters interpret model and store
model information in PIM document (see Figure 4), fa-
cilitating data transition and access.
Figure 4. Model Interpretation PIM document
Figure 5. Code generation architecture
Figure 6. Process of code generation
4.2 Based on Template Code Generation
In the process of code generation, we use different tem-
plates to meet the needs of different platforms, which is
similar to macros [11]. If there is no available template
for specific application, the application developer can
develop new templates according to the requirements. In
this way, we can guarantee that the code supports differ-
ent platforms (Figure 5).
The workflow of code generation engine consists of
two steps (see Figure 6). Firstly, invoking xml DOM
parser to parse PIM and building DOM tree. Secondly,
parsing template contents and replacing template con-
tents with DOM tree.
5. Conclusions
In this paper, we present a solution for developing the
embedded control system. Relevant meta-model and
model are expressed by class diagrams and a set of OCL
constraints. A model interpreter is developed and the
model information is interpreted, which is stored in PIM
document. According to the system requirements, if we
need new functions and want to support cross-platform,
we have to construct new templates. This solution speeds
up the application development and reduces the cost.
What’s more, the application is easy to maintain by the
modified meta-model.
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