A Solution Based on Modeling and Code Generation for Embedded Control System

doi:10.4236/jsea.2009.23023

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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.

Email: wugh@hdu.edu.cn, cdw8411@163.com, zw h n z @ zj 1 6 5.com

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

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),

S mapping relates

syntactic concepts to the semantic domain.

5) Syntactic mapping (Mc),

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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