Performance of Genetic Algorithms in the Context of Software Model Refactoring
Fuente:
arXiv
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Preprint |
| Published: |
2023
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866910310674202624 |
|---|---|
| author | Cortellessa, Vittorio Di Pompeo, Daniele Tucci, Michele |
| author_facet | Cortellessa, Vittorio Di Pompeo, Daniele Tucci, Michele |
| contents | Software systems continuously evolve due to new functionalities, requirements, or maintenance activities. In the context of software evolution, software refactoring has gained a strategic relevance. The space of possible software refactoring is usually very large, as it is given by the combinations of different refactoring actions that can produce software system alternatives. Multi-objective algorithms have shown the ability to discover alternatives by pursuing different objectives simultaneously. Performance of such algorithms in the context of software model refactoring is of paramount importance. Therefore, in this paper, we conduct a performance analysis of three genetic algorithms to compare them in terms of performance and quality of solutions. Our results show that there are significant differences in performance among the algorithms (e.g., PESA2 seems to be the fastest one, while NSGA-II shows the least memory usage). |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2308_13875 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Performance of Genetic Algorithms in the Context of Software Model Refactoring Cortellessa, Vittorio Di Pompeo, Daniele Tucci, Michele Software Engineering Performance Software systems continuously evolve due to new functionalities, requirements, or maintenance activities. In the context of software evolution, software refactoring has gained a strategic relevance. The space of possible software refactoring is usually very large, as it is given by the combinations of different refactoring actions that can produce software system alternatives. Multi-objective algorithms have shown the ability to discover alternatives by pursuing different objectives simultaneously. Performance of such algorithms in the context of software model refactoring is of paramount importance. Therefore, in this paper, we conduct a performance analysis of three genetic algorithms to compare them in terms of performance and quality of solutions. Our results show that there are significant differences in performance among the algorithms (e.g., PESA2 seems to be the fastest one, while NSGA-II shows the least memory usage). |
| title | Performance of Genetic Algorithms in the Context of Software Model Refactoring |
| topic | Software Engineering Performance |
| url | https://arxiv.org/abs/2308.13875 |