CONTENTS

224 CAMELIA S¸ERBAN (1)
2. Theoretical background
Object oriented design quality evaluation implies identification of those design
entities that are relevant for the analysis and of the software metrics that best emphasize
the aspects (design priciple/heuristics) that we want to quantify, and the
interpretation of the measurements results obtained. These elements were described
in detail in our previous work [9]. In what follows we will not get into their details,
but we will try to list them, addapted to our current problem.
2.1. A meta-model for object-oriented systems. In [4] a meta-model for objectoriented
systems is defined as a 3-tuple, S = (E, P, R) where, E represents the set
of design entities of the software system ( classes, methods, attributes, parameters
or local variables), P represents the set of properties of the aforementioned design
entities (e.g. abstraction, visibility, reusability, reuse, binding) and R represents the
set of relations between the entities of set E (e.g methods access).
2.2. Some relevant coupling metrics. A significant number of coupling metrics
have been defined in the literature [10, 11, 12]. Because, we aim to identify those
classes in which a change would significantly affect many other places in the sourcecode,
we select coupling metrics that better emphasize this aspect.
Changing Methods (CM) is defined as the number of distinct methods in the system
that would be potentially affected by changes operated in the measured class.
Weighted Changing Methods (WCM) [4]. WCM is computed as the sum of the
“weights”(number of distinct members from the server-class that are referenced in
a method) of all the methods affected by changes operated in the measured class.
Changing Classes (CC). The CC metric is defined as the number of client-classes
where the changes must be operated as the result of a change in the server-class.
2.3. Fuzzy clustering analisys. Consider C = {c1, c2, ..., cl}, C ⊂ E, the set of
all classes from the meta-model defined in Section 2.1. Each class, ci from C set is
described by a 3-dimensional vector, ci = (cm, wcm, cc), where the components of ci
vector are the computed values for the metrics defined before.
Next, our goal is to group similar classes, in order to obtained a list of “suspects”
(classes tightly coupled).
Because is hard to establish thresholds for the applied measurements, we used a
fuzzy clustering analysis. An object will belong to more than one class with different
membership degree. Also, in order to determine the number of clusters, which is a
data entry for a fuzzy clustering generic algorithm, we considered the Fuzzy Divisive
Hierarchic Clustering (FDHC) algorithm [7]. This algorithm produce not only
the optimal number of classes (based on the needed granularity), but also a binary
hierarchy that show the existing relationships between the classes.
3. Case study
In order to validate our approach we have used the following case study. The
system proposed for evaluation is log4net [3], an open source application. It consists
of 214 classes. The elements from the meta-model described in Section 2.1 have been