Genetic algorithms and heuristics hybridized for software architecture recovery

Name: Genetic algorithms and heuristics hybridized for software architecture recovery
Author: Elyasi, Milad, Simitcioğlu, Muhammed Esad, Saydemir, Abdullah, Ekici, Ali, Özener, Okan Örsan, Sözer, Hasan

İsim	Genetic algorithms and heuristics hybridized for software architecture recovery
Yazar	Elyasi, Milad, Simitcioğlu, Muhammed Esad, Saydemir, Abdullah, Ekici, Ali, Özener, Okan Örsan, Sözer, Hasan
Basım Tarihi:	2023-06-26
Basım Yeri	- Springer
Konu	Genetic algorithms, Reverse engineering, Software architecture recovery, Software modularity, Software module clustering
Tür	Süreli Yayın
Dil	İngilizce
Dijital	Evet
Yazma	Hayır
Kütüphane:	Özyeğin Üniversitesi
Demirbaş Numarası	0928-8910
Kayıt Numarası	54a939fa-f09c-4247-a765-46f3ffc22da2
Lokasyon	Industrial Engineering, Computer Science
Tarih	2023-06-26
Notlar	TÜBİTAK
Örnek Metin	Large scale software systems must be decomposed into modular units to reduce maintenance efforts. Software Architecture Recovery (SAR) approaches have been introduced to analyze dependencies among software modules and automatically cluster them to achieve high modularity. These approaches employ various types of algorithms for clustering software modules. In this paper, we discuss design decisions and variations in existing genetic algorithms devised for SAR. We present a novel hybrid genetic algorithm that introduces three major differences with respect to these algorithms. First, it employs a greedy heuristic algorithm to automatically determine the number of clusters and enrich the initial population that is generated randomly. Second, it uses a different solution representation that facilitates an arithmetic crossover operator. Third, it is hybridized with a heuristic that improves solutions in each iteration. We present an empirical evaluation with seven real systems as experimental objects. We compare the effectiveness of our algorithm with respect to a baseline and state-of-the-art hybrid genetic algorithms. Our algorithm outperforms others in maximizing the modularity of the obtained clusters.
DOI	10.1007/s10515-023-00384-y
Cilt	30

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Genetic algorithms and heuristics hybridized for software architecture recovery

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

Basım Tarihi 2023-06-26

Basım Yeri - Springer

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

Tür Süreli Yayın

Dil İngilizce

Dijital Evet

Yazma Hayır

Kütüphane Özyeğin Üniversitesi

Demirbaş Numarası 0928-8910

Kayıt Numarası 54a939fa-f09c-4247-a765-46f3ffc22da2

Lokasyon Industrial Engineering, Computer Science

Tarih 2023-06-26

Notlar TÜBİTAK

Örnek Metin Large scale software systems must be decomposed into modular units to reduce maintenance efforts. Software Architecture Recovery (SAR) approaches have been introduced to analyze dependencies among software modules and automatically cluster them to achieve high modularity. These approaches employ various types of algorithms for clustering software modules. In this paper, we discuss design decisions and variations in existing genetic algorithms devised for SAR. We present a novel hybrid genetic algorithm that introduces three major differences with respect to these algorithms. First, it employs a greedy heuristic algorithm to automatically determine the number of clusters and enrich the initial population that is generated randomly. Second, it uses a different solution representation that facilitates an arithmetic crossover operator. Third, it is hybridized with a heuristic that improves solutions in each iteration. We present an empirical evaluation with seven real systems as experimental objects. We compare the effectiveness of our algorithm with respect to a baseline and state-of-the-art hybrid genetic algorithms. Our algorithm outperforms others in maximizing the modularity of the obtained clusters.

DOI 10.1007/s10515-023-00384-y

Cilt 30