SEKE 2012 Proceedings - Knowledge Systems Institute

Identification of Design Patterns Using Dependence
Analysis
Wentao Ma, Xiaoyu Zhou, Xiaofang Qi
School of Computer Science and Engineering
Southeast University
Key Lab of Computer Network and Information Integration
(Southeast University), Ministry of Education
Nanjing, China
Ju Qian
College of Computer Science and Technology
Nanjing University of Aeronautics and Astronautic
Nanjing, China
Lei Xu, Rui Yang
Department of Computer Science and Technology
Nanjing University
Nanjing, China
Abstract—Identification of design pattern instances in source
codes facilitates software understanding and maintenance. Existing
researches use three kinds of information in the identification:
structure, control flow, and class or method names. In this paper,
data flow information is used in identification. An approach is
presented to identify instances of Adapter, State, Strategy and
Observer patterns based on program dependence analysis. It
brings more precise and detailed identification result. The effectiveness
of the approach is illustrated by experiments on open
source programs.
Keywords-design pattern; program dependence; program
comprehension; reverse engineering;
I. INTRODUCTION
GoF design patterns are widely used in practice [1]. Identification
of design pattern instances (hereinafter referred to as
"pattern instances" for short) in source codes recovers the design-code
traceability, facilitates software understanding and
maintenance.
Ideally, identification of pattern instances should identify
all the classes, methods and variables that play roles in the design
patterns.
Early researches focus on identifying pattern instances by
structure information [9]. These approaches cannot distinguish
design patterns having similar structures, such as State and
Strategy pattern [5][6]. They are also not good at identifying
methods playing roles in patterns.
Supported partially by the National Natural Science Foundation of China
under Grant No. 90818027, and No. 60903026, and No. 61003156, and
partially supported by the Fundamental Research Funds for the Central
Universities under Grant No.1116020205, and No.1118020203.
Correspondence to: Xiaoyu Zhou, School of Computer Science and Engineering,
Southeast University, Nanjing, China. E-mail: zhouxy@seu.edu.cn.
Recently, some researches identify pattern instances by
checking whether call sequences of the analyzed source codes
are in accordance with that of the design patterns [7][11]. These
approaches are more capable in identifying methods of design
patterns. But they can't identify design patterns that have
no special characteristics in method call sequences, such as
Adapter pattern [5][8][13].
Some few researchers identify pattern instances using semantics
of th e classes or methods names [10][12]. These approaches
are not mainstream in pattern instance identification
research.
Existing researches can only identified two kinds of variables
that play roles in design patterns: (1) field of one class that
refers to another class in the pattern instance; (2) method parameter
or return value whose type is the class in the pattern
instances. Many other variables playing roles in design patterns
cannot be identified in these researches.
Design patterns should be identified from multiple perspectives.
The more perspectives are used, the more precise and
detailed result will be obtained. As far as we know, except for
our own work [2], data flow information has not yet been used
in design pattern identification research. In this paper, we identify
pattern instances using dependence relations in the analyzed
codes. In this paper:
1) We present the characteristic of dependence relations in
adapting method. Both Adapter and State pattern has adapting
method, but Strategy pattern doesn't. Based on the
distinguishment, we identify Adapter pattern instances, and
distinguish State pattern instances from that of Strategy.
2) Dependence relations of variables within Notify method
of Observer pattern is pres ented. Using the relations, we
identify Observer instances and some variables playing roles
in the pattern, such as the event objects notified to Observers,
and the variables that act as targets or results of observation.
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