Evaluating the effectiveness of multi-level greedy modularity clustering for software architecture recovery

Name: Evaluating the effectiveness of multi-level greedy modularity clustering for software architecture recovery
Author: Sözer, Hasan

İsim	Evaluating the effectiveness of multi-level greedy modularity clustering for software architecture recovery
Yazar	Sözer, Hasan
Basım Tarihi:	2019
Basım Yeri	- Springer Nature
Konu	Software architecture recovery, Software architecture reconstruction, Reverse engineering, Modularity clustering, Empirical evaluation
Tür	Belge
Dil	İngilizce
Dijital	Evet
Yazma	Hayır
Kütüphane:	Özyeğin Üniversitesi
Demirbaş Numarası	978-303029982-8
Kayıt Numarası	487ad78c-d087-4906-9d74-4625074995a2
Lokasyon	Computer Science
Tarih	2019
Örnek Metin	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

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Evaluating the effectiveness of multi-level greedy modularity clustering for software architecture recovery

Yazar Sözer, Hasan

Basım Tarihi 2019

Basım Yeri - Springer Nature

Konu Software architecture recovery, Software architecture reconstruction, Reverse engineering, Modularity clustering, Empirical evaluation

Tür Belge

Dil İngilizce

Dijital Evet

Yazma Hayır

Kütüphane Özyeğin Üniversitesi

Demirbaş Numarası 978-303029982-8

Kayıt Numarası 487ad78c-d087-4906-9d74-4625074995a2

Lokasyon Computer Science

Tarih 2019

Örnek Metin 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