1 results listed
Manual categorization of applications in software repositories such as SourceForge is
often time-consuming and error-prone. Automation of this process not only simplifies the daily
task of administrators but also helps project owners to add their projects into the corresponding
subcategory of the repository without any delay. In this study, we propose a cloudbased application
categorization system that applies machine learning algorithms to support the classification of
applications. The categorization system has a web-based client application to parse, process, and
submit the project source code, a web service which automatically performs classification of
applications into domain categories, and a cloud-computing platform which hosts the
categorization service. Several multi-class classification algorithms have been adopted including,
Artificial Neural Networks, Logistic Regression, Decision Jungle, and Decision Forest algorithms
to validate the effectiveness of the system in multiple case studies. The case studies were
performed on three public datasets generated based on 3286 Java applications of SourceForge
repository. Our study shows that the highest accuracy was achieved with Artificial Neural
Networks (ANN) algorithm. The resulting prediction model has been transformed into a web
service and then, deployed on the Azure cloud platform.
International Data Science & Engineering Symposium
IDSES
Çağatay ÇATAL
Besme ELNACCAR
Özge ÇOLAKOĞLU
Bedir TEKİNERDOĞAN1