HYGAR: a hybrid genetic algorithm for software architecture recovery

Name: HYGAR: a hybrid genetic algorithm for software architecture recovery
Author: Elyasi, Milad, Simitcioğlu, Muhammed Esad, Saydemir, Abdullah, Ekici, Ali, Sözer, Hasan

İsim	HYGAR: a hybrid genetic algorithm for software architecture recovery
Yazar	Elyasi, Milad, Simitcioğlu, Muhammed Esad, Saydemir, Abdullah, Ekici, Ali, Sözer, Hasan
Basım Tarihi:	2022
Basım Yeri	- ACM
Konu	Genetic algorithms, Reverse engineering, Software architecture recovery, Software modularity, Software module clustering
Tür	Belge
Dil	İngilizce
Dijital	Evet
Yazma	Hayır
Kütüphane:	Özyeğin Üniversitesi
Demirbaş Numarası	2-s2.0-85130329885
Kayıt Numarası	255ba84f-ed2a-4a63-916e-86d036d2e641
Lokasyon	Industrial Engineering, Computer Science
Tarih	2022
Notlar	TÜBİTAK
Örnek Metin	Genetic algorithms have been used for clustering modules of a software system in line with the modularity principle. The goal of these algorithms is to recover an architectural view in the form of a modular structural decomposition of the system. We discuss design decisions and variations in existing genetic algorithms devised for this purpose. We introduce HYGAR, a novel hybrid variant of existing algorithms. We apply HYGAR for software architecture recovery of 5 real systems and compare its effectiveness with respect to a baseline and a state-of-the-art hybrid algorithm. Results show that HYGAR outperforms these algorithms in maximizing the modularity of the obtained clustering.
DOI	10.1145/3477314.3507020

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HYGAR: a hybrid genetic algorithm for software architecture recovery

Yazar Elyasi, Milad, Simitcioğlu, Muhammed Esad, Saydemir, Abdullah, Ekici, Ali, Sözer, Hasan

Basım Tarihi 2022

Basım Yeri - ACM

Konu Genetic algorithms, Reverse engineering, Software architecture recovery, Software modularity, Software module clustering

Tür Belge

Dil İngilizce

Dijital Evet

Yazma Hayır

Kütüphane Özyeğin Üniversitesi

Demirbaş Numarası 2-s2.0-85130329885

Kayıt Numarası 255ba84f-ed2a-4a63-916e-86d036d2e641

Lokasyon Industrial Engineering, Computer Science

Tarih 2022

Notlar TÜBİTAK

Örnek Metin Genetic algorithms have been used for clustering modules of a software system in line with the modularity principle. The goal of these algorithms is to recover an architectural view in the form of a modular structural decomposition of the system. We discuss design decisions and variations in existing genetic algorithms devised for this purpose. We introduce HYGAR, a novel hybrid variant of existing algorithms. We apply HYGAR for software architecture recovery of 5 real systems and compare its effectiveness with respect to a baseline and a state-of-the-art hybrid algorithm. Results show that HYGAR outperforms these algorithms in maximizing the modularity of the obtained clustering.

DOI 10.1145/3477314.3507020