Author
Elyasi, Milad, Simitcioğlu, Muhammed Esad, Saydemir, Abdullah, Ekici, Ali, Sözer, Hasan
Publication Date
2022
Publication Place
-
ACM
Subject
Genetic algorithms, Reverse engineering, Software architecture recovery, Software modularity, Software module clustering
Type
Document
Language
English
Digital
Yes
Manuscript
No
Library
Özyeğin University
Library Asset ID
2-s2.0-85130329885
Record ID
255ba84f-ed2a-4a63-916e-86d036d2e641
Library Location
Industrial Engineering, Computer Science
Date
2022
Notes
TÜBİTAK
Sample Text
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
