Component-based software development has been regarded as one of the
newest trends in the software development industry. In some ways,
component-based software development focuses on reusability. In fact,
high-quality software components refer to components that are highly
cohesion internally, and also have the least coupling with other
components, or in other words, are independent. Thus, using such
components will lead to faster software development. In this way, this
study aims to identify reusable components in software. To do this,
first, software components were extracted from the source code of
software systems. To identify reusable components, an artificial neural
network algorithm has been used. On the other hand, the reusable
components recognized by the evolutionary particle swarm optimization
algorithm have been clustered. In this study, the particle swarm
optimization algorithm was modified to apply to clustering problems.
Finally, with the help of these clusters, the KNN classifier was
trained, and during identifying reused inputs, the cluster to which the
input belongs was determined by the classifier. This way, we will have a
library of reusable components, where similar components are placed in a
cluster. The proposed algorithm was experimentally evaluated on nine
open-source software systems belonging to different domains. The results
of this study show that the counted software components are potentially
reusable and confirm the research findings.