Author
Sözer, Hasan
Publication Date
2019
Publication Place
-
Springer Nature
Subject
Software architecture recovery, Software architecture reconstruction, Reverse engineering, Modularity clustering, Empirical evaluation
Type
Document
Language
English
Digital
Yes
Manuscript
No
Library
Özyeğin University
Library Asset ID
978-303029982-8
Record ID
487ad78c-d087-4906-9d74-4625074995a2
Library Location
Computer Science
Date
2019
Sample Text
Software architecture recovery approaches mainly analyze various types of dependencies among software modules to group them and reason about the high-level structural decomposition of a system. These approaches employ a variety of clustering techniques. In this paper, we present an empirical evaluation of a modularity clustering technique used for software architecture recovery. We use five open source projects as subject systems for which the ground-truth architectures were known. This dataset was previously prepared and used in an empirical study for evaluating four state-of-the-art architecture recovery approaches and their variants as well as two baseline clustering algorithms. We used the same dataset for an evaluation of multi-level greedy modularity clustering. Results showed that MGMC outperforms all the other SAR approaches in terms of accuracy and modularization quality for most of the studied systems. In addition, it scales better to very large systems for which it runs orders-of-magnitude faster than all the other algorithms.
Editör
Bures, T., Duchien, L., Inverardi, P.
DOI
10.1007/978-3-030-29983-5_5
Cilt
11681
