Optimization of use case point through the use of metaheuristic algorithm in estimating software effort
Use Case Points estimation framework relies on the complexity weight parameters to estimate software development projects. However, due to the discontinue parameters, it lead to abrupt weight classification and results in inaccurate estimation. Several research studies have addressed these weaknesse...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Universitas Ahmad Dahlan
2024-02-01
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| Series: | IJAIN (International Journal of Advances in Intelligent Informatics) |
| Subjects: | |
| Online Access: | http://ijain.org/index.php/IJAIN/article/view/1298 |
| _version_ | 1797269095082622976 |
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| author | Ardiansyah Ardiansyah Mulki Indana Zulfa Ali Tarmuji Farisna Hamid Jabbar |
| author_facet | Ardiansyah Ardiansyah Mulki Indana Zulfa Ali Tarmuji Farisna Hamid Jabbar |
| author_sort | Ardiansyah Ardiansyah |
| collection | DOAJ |
| description | Use Case Points estimation framework relies on the complexity weight parameters to estimate software development projects. However, due to the discontinue parameters, it lead to abrupt weight classification and results in inaccurate estimation. Several research studies have addressed these weaknesses by employing various approaches, including fuzzy logic, regression analysis, and optimization techniques. Nevertheless, the utilization of optimization techniques to determine use case weight parameter values has yet to be extensively explored, with the potential to enhance accuracy further. Motivated by this, the current research delves into various metaheuristic search-based algorithms, such as genetic algorithms, Firefly algorithms, Reptile search algorithms, Particle swarm optimization, and Grey Wolf optimizers. The experimental investigation was carried out using a Silhavy UCP estimation dataset, which contains 71 project data from three software houses and is publicly available. Furthermore, we compared the performance between models based on metaheuristic algorithms. The findings indicate that the performance of the Firefly algorithm outperforms the others based on five accuracy metrics: mean absolute error, mean balance relative error, mean inverted relative error, standardized accuracy, and effect size. |
| first_indexed | 2024-04-25T01:42:55Z |
| format | Article |
| id | doaj.art-25bd1a8f7bc948be8ecac4d1c70f8ef1 |
| institution | Directory Open Access Journal |
| issn | 2442-6571 2548-3161 |
| language | English |
| last_indexed | 2024-04-25T01:42:55Z |
| publishDate | 2024-02-01 |
| publisher | Universitas Ahmad Dahlan |
| record_format | Article |
| series | IJAIN (International Journal of Advances in Intelligent Informatics) |
| spelling | doaj.art-25bd1a8f7bc948be8ecac4d1c70f8ef12024-03-08T03:14:05ZengUniversitas Ahmad DahlanIJAIN (International Journal of Advances in Intelligent Informatics)2442-65712548-31612024-02-0110110913010.26555/ijain.v10i1.1298282Optimization of use case point through the use of metaheuristic algorithm in estimating software effortArdiansyah Ardiansyah0Mulki Indana Zulfa1Ali Tarmuji2Farisna Hamid Jabbar3Informatics Department, Universitas Ahmad DahlanDepartment of Electrical Engineering, Jenderal Soedirman UniversityInformatics Department, Universitas Ahmad DahlanInformatics Department, Universitas Ahmad DahlanUse Case Points estimation framework relies on the complexity weight parameters to estimate software development projects. However, due to the discontinue parameters, it lead to abrupt weight classification and results in inaccurate estimation. Several research studies have addressed these weaknesses by employing various approaches, including fuzzy logic, regression analysis, and optimization techniques. Nevertheless, the utilization of optimization techniques to determine use case weight parameter values has yet to be extensively explored, with the potential to enhance accuracy further. Motivated by this, the current research delves into various metaheuristic search-based algorithms, such as genetic algorithms, Firefly algorithms, Reptile search algorithms, Particle swarm optimization, and Grey Wolf optimizers. The experimental investigation was carried out using a Silhavy UCP estimation dataset, which contains 71 project data from three software houses and is publicly available. Furthermore, we compared the performance between models based on metaheuristic algorithms. The findings indicate that the performance of the Firefly algorithm outperforms the others based on five accuracy metrics: mean absolute error, mean balance relative error, mean inverted relative error, standardized accuracy, and effect size.http://ijain.org/index.php/IJAIN/article/view/1298software effort estimation, optimization, metaheuristics, use case points |
| spellingShingle | Ardiansyah Ardiansyah Mulki Indana Zulfa Ali Tarmuji Farisna Hamid Jabbar Optimization of use case point through the use of metaheuristic algorithm in estimating software effort IJAIN (International Journal of Advances in Intelligent Informatics) software effort estimation, optimization, metaheuristics, use case points |
| title | Optimization of use case point through the use of metaheuristic algorithm in estimating software effort |
| title_full | Optimization of use case point through the use of metaheuristic algorithm in estimating software effort |
| title_fullStr | Optimization of use case point through the use of metaheuristic algorithm in estimating software effort |
| title_full_unstemmed | Optimization of use case point through the use of metaheuristic algorithm in estimating software effort |
| title_short | Optimization of use case point through the use of metaheuristic algorithm in estimating software effort |
| title_sort | optimization of use case point through the use of metaheuristic algorithm in estimating software effort |
| topic | software effort estimation, optimization, metaheuristics, use case points |
| url | http://ijain.org/index.php/IJAIN/article/view/1298 |
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