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
