Relating Clusterization Measures and Software Quality
Béla Csaba, Lajos Schrettner, Árpád
Beszédes, Judit Jász, Péter Hegedűs and
Empirical studies have shown that dependence clusters
are both prevalent in source code and detrimental to many
activities related to software, including maintenance, testing and
comprehension. Based on such observations, it would be worthwhile
to try to give a more precise characterization of the connection
between dependence clusters and software quality. Such attempts
are hindered by a number of difficulties: there are problems in
assessing the quality of software, measuring the degree of
clusterization of software and finding the means to exhibit the
connection (or lack of it) between the two.
In this paper we present our approach to establish a connection
between software quality and clusterization. Software quality
models comprise of low- and high-level quality attributes, in
addition we defined new clusterization metrics that give a concise
characterization of the clusters contained in programs. Apart from
calculating correlation coefficients, we used mutual information
to quantify the relationship beetween clusterization and quality.
Results show that a connection can be demonstrated between the
two, and that mutual information combined with correlation can be
a better indicator to conduct deeper examinations in the area.
quality model, Quality metrics, Dependence cluster, Clusterization
metrics, Correlation, Mutual information.