CONTENTS

KNOWLEDGE ENGINEERING: PRINCIPLES AND TECHNIQUES
Proceedings of the International Conference on Knowledge Engineering,
Principles and Techniques, KEPT2009
Cluj-Napoca (Romania), July 2–4, 2009, pp. 223–226
HIGH COUPLING DETECTION USING FUZZY CLUSTERING
ANALYSIS
CAMELIA S¸ERBAN (1)
Abstract. Coupling is an important criterion when evaluating a software design
because it captures a very desirable characteristic: a change to one part of a system
should have a minimal impact on other parts. An excessive coupling plays a
negative role on many external quality attributes like reusability, maintainability
and testability.
This paper aims at presenting a new approach concerning the identification
of those classes with high coupling, from an object oriented system.
1. Introduction
In order to keep the software system easy to maintain, the assessment of its design
according to well established principle and heuristics, has to be made through
the entire development lifecycle. The principle of low coupling is reflected by all the
authors [2, 3, 5] that propose design rules and heuristics for object-oriented programming.
They all converge in saying that coupling should be kept low. A change to
one part of a system it is important to have a minimal impact on other parts. Those
design entities that could propagate a lot of changes in the system, if something has
been changed inside them, have to be identified and reviewed.
In this article, we our goal is to identify the design entities that are strongly
coupled with the rest of the system. The approach is based on fuzzy clustering
analysis. Some metrics are used in order to quantify those aspects consired important
with regard to coupling.
The remainder of this paper is organized as follows. Section 2 describes the
theoretical background for our approach. Section 3 presents and discusses the experimental
results obtained by applying the proposed approach on an open source
application, called log4net [8]. Section 4 reviews related works in the area of detection
design flaws. Finally, Section 5 summarizes the contributions of this work and
outlines directions for further research.
2000 Mathematics Subject Classification. 68N30, 03E72.
Key words and phrases. coupling, metrics, fuzzy analisys.
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c○2009 Babe¸s-Bolyai University, Cluj-Napoca