CONTENTS

TRANSFORMING PROCEDURAL INTO OBJECT-ORIENTED SOFTWARE SYSTEMS 187
We have chosen the distance between two entities as expressed in Equation (1)
because it emphasizes the idea of cohesion. As illustrated in [4], “Cohesion refers to
the degree to which module components belong together”. Our distance, as defined in
Equation (1), highlights the concept of cohesion. It is very likely that entities with
low distances have to be placed in the same application class, and distant entities
have to belong to different aplication classes.
kOOS algorithm is a k-medois like algorithm that uses a heuristic for choosing
the initial medoids.
The entity chosen as the first medoid is the most “distant” entity from the set of
all entities (the entity that maximizes the sum of distances from all other entities).
At each step we select from the remaining entities the most distant entity relative
to the already chosen medoids. If the selected entity is close enough to the already
chosen medoids, then the process is stoped, otherwise the selected entity is added to
the medoids list and we try to choose a new medoid from the remaining entities.
We consider an entity close to a set of medoids if the distances between the entity
and any of the medoids are less than a given threshold. We have chosen the value
1 for the threshold because distances greater than 1 are obtained only for unrelated
entities (Equation (1)).
The idea of the above described heuristic is to choose medoids that will act as
seeds for the clusters that will represent application classes in the resulted objectoriented
system.
After selecting the initial medoids, kOOS behaves like the classical k-medoids
algorithm.
The main idea of the kOOS algorithm that we apply in order to group entities
from a software system is the following:
(i) The initial number p of clusters and the initial medoids are determined by
the heuristic described above.
(ii) The clusters are recalculated, i.e., each object is assigned to the closest
medoid. Empty clusters will be eliminated from the partition.
(iii) Recalculate the medoid i of each cluster k based on the following idea: if h is
an object from k such that
(d(j, h) − d(j, i)) is negative, then h becomes
j∈k
the new medoid of cluster k.
(iv) Steps (ii)-(iii) are repeatedly performed until there is no change in the partition
K.
Each cluster from the resulted partition will represent an application class from
the equivalent object-oriented version of the software system S.
We have successfully evaluated our approach on a simple code example written
in Borland Pascal.
3. Conclusions and further work
We have presented in this paper a partitional clustering algorithm (kOOS) that
can be used for assisting software developers in transforming procedural software