Performance evaluation metrics for multi-objective evolutionary algorithms in search-based software engineering: systematic literature review
Many recent studies have shown that various multi-objective evolutionary algorithms have been widely applied in the field of search-based software engineering (SBSE) for optimal solutions. Most of them either focused on solving newly re-formulated problems or on proposing new approaches, while a num...
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| Format: | Article |
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MDPI AG
2021
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| _version_ | 1796983005158309888 |
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| author | Nuh, Jamal Abdullahi Koh, Tieng Wei Baharom, Salmi Osman, Mohd Hafeez Kew, Si Na |
| author_facet | Nuh, Jamal Abdullahi Koh, Tieng Wei Baharom, Salmi Osman, Mohd Hafeez Kew, Si Na |
| author_sort | Nuh, Jamal Abdullahi |
| collection | UPM |
| description | Many recent studies have shown that various multi-objective evolutionary algorithms have been widely applied in the field of search-based software engineering (SBSE) for optimal solutions. Most of them either focused on solving newly re-formulated problems or on proposing new approaches, while a number of studies performed reviews and comparative studies on the performance of proposed algorithms. To evaluate such performance, it is necessary to consider a number of performance metrics that play important roles during the evaluation and comparison of investigated algorithms based on their best-simulated results. While there are hundreds of performance metrics in the literature that can quantify in performing such tasks, there is a lack of systematic review conducted to provide evidence of using these performance metrics, particularly in the software engineering problem domain. In this paper, we aimed to review and quantify the type of performance metrics, number of objectives, and applied areas in software engineering that reported in primary studies—this will eventually lead to inspiring the SBSE community to further explore such approaches in depth. To perform this task, a formal systematic review protocol was applied for planning, searching, and extracting the desired elements from the studies. After considering all the relevant inclusion and exclusion criteria for the searching process, 105 relevant articles were identified from the targeted online databases as scientific evidence to answer the eight research questions. The preliminary results show that remarkable studies were reported without considering performance metrics for the purpose of algorithm evaluation. Based on the 27 performance metrics that were identified, hypervolume, inverted generational distance, generational distance, and hypercube-based diversity metrics appear to be widely adopted in most of the studies in software requirements engineering, software design, software project management, software testing, and software verification. Additionally, there are increasing interest in the community in re-formulating many objective problems with more than three objectives, yet, currently are dominated in re-formulating two to three objectives. |
| first_indexed | 2024-03-06T11:00:11Z |
| format | Article |
| id | upm.eprints-94541 |
| institution | Universiti Putra Malaysia |
| last_indexed | 2024-03-06T11:00:11Z |
| publishDate | 2021 |
| publisher | MDPI AG |
| record_format | dspace |
| spelling | upm.eprints-945412022-12-02T07:58:31Z http://psasir.upm.edu.my/id/eprint/94541/ Performance evaluation metrics for multi-objective evolutionary algorithms in search-based software engineering: systematic literature review Nuh, Jamal Abdullahi Koh, Tieng Wei Baharom, Salmi Osman, Mohd Hafeez Kew, Si Na Many recent studies have shown that various multi-objective evolutionary algorithms have been widely applied in the field of search-based software engineering (SBSE) for optimal solutions. Most of them either focused on solving newly re-formulated problems or on proposing new approaches, while a number of studies performed reviews and comparative studies on the performance of proposed algorithms. To evaluate such performance, it is necessary to consider a number of performance metrics that play important roles during the evaluation and comparison of investigated algorithms based on their best-simulated results. While there are hundreds of performance metrics in the literature that can quantify in performing such tasks, there is a lack of systematic review conducted to provide evidence of using these performance metrics, particularly in the software engineering problem domain. In this paper, we aimed to review and quantify the type of performance metrics, number of objectives, and applied areas in software engineering that reported in primary studies—this will eventually lead to inspiring the SBSE community to further explore such approaches in depth. To perform this task, a formal systematic review protocol was applied for planning, searching, and extracting the desired elements from the studies. After considering all the relevant inclusion and exclusion criteria for the searching process, 105 relevant articles were identified from the targeted online databases as scientific evidence to answer the eight research questions. The preliminary results show that remarkable studies were reported without considering performance metrics for the purpose of algorithm evaluation. Based on the 27 performance metrics that were identified, hypervolume, inverted generational distance, generational distance, and hypercube-based diversity metrics appear to be widely adopted in most of the studies in software requirements engineering, software design, software project management, software testing, and software verification. Additionally, there are increasing interest in the community in re-formulating many objective problems with more than three objectives, yet, currently are dominated in re-formulating two to three objectives. MDPI AG 2021-03 Article PeerReviewed Nuh, Jamal Abdullahi and Koh, Tieng Wei and Baharom, Salmi and Osman, Mohd Hafeez and Kew, Si Na (2021) Performance evaluation metrics for multi-objective evolutionary algorithms in search-based software engineering: systematic literature review. Applied Sciences, 11 (7). art. no. 3117. pp. 1-25. ISSN 2076-3417 https://www.mdpi.com/2076-3417/11/7/3117 10.3390/app11073117 |
| spellingShingle | Nuh, Jamal Abdullahi Koh, Tieng Wei Baharom, Salmi Osman, Mohd Hafeez Kew, Si Na Performance evaluation metrics for multi-objective evolutionary algorithms in search-based software engineering: systematic literature review |
| title | Performance evaluation metrics for multi-objective evolutionary algorithms in search-based software engineering: systematic literature review |
| title_full | Performance evaluation metrics for multi-objective evolutionary algorithms in search-based software engineering: systematic literature review |
| title_fullStr | Performance evaluation metrics for multi-objective evolutionary algorithms in search-based software engineering: systematic literature review |
| title_full_unstemmed | Performance evaluation metrics for multi-objective evolutionary algorithms in search-based software engineering: systematic literature review |
| title_short | Performance evaluation metrics for multi-objective evolutionary algorithms in search-based software engineering: systematic literature review |
| title_sort | performance evaluation metrics for multi objective evolutionary algorithms in search based software engineering systematic literature review |
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