Optimizing complexity weight parameter of use case points estimation using particle swarm optimization
Among algorithmic-based frameworks for software development effort estimation, Use Case Points I s one of the most used. Use Case Points is a well-known estimation framework designed mainly for object-oriented projects. Use Case Points uses the use case complexity weight as its essential parameter....
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Universitas Ahmad Dahlan
2022
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id-ugm-repo.2820972023-12-01T01:07:48Z https://repository.ugm.ac.id/282097/ Optimizing complexity weight parameter of use case points estimation using particle swarm optimization Ardiansyah, Ardiansyah Ferdiana, Ridi Permanasari, Adhistya Erna Electrical and Electronic Engineering not elsewhere classified Among algorithmic-based frameworks for software development effort estimation, Use Case Points I s one of the most used. Use Case Points is a well-known estimation framework designed mainly for object-oriented projects. Use Case Points uses the use case complexity weight as its essential parameter. The parameter is calculated with the number of actors and transactions of the use case. Nevertheless, use case complexity weight is discontinuous, which can sometimes result in inaccurate measurements and abrupt classification of the use case. The objective of this work is to investigate the potential of integrating particle swarm optimization (PSO) with the Use Case Points framework. The optimizer algorithm is utilized to optimize the modified use case complexity weight parameter. We designed and conducted an experiment based on real-life data set from three software houses. The proposed model’s accuracy and performance evaluation metric is compared with other published results, which are standardized accuracy, effect size, mean balanced residual error, mean inverted balanced residual error, and mean absolute error. Moreover, the existing models as the benchmark are polynomial regression, multiple linear regression, weighted case-based reasoning with (PSO), fuzzy use case points, and standard Use Case Points. Experimental results show that the proposed model generates the best value of standardized accuracy of 99.27 and an effect size of 1.15 over the benchmark models. The results of our study are promising for researchers and practitioners because the proposed model is actually estimating, not guessing, and generating meaningful estimation with statistically and practically significant. © 2022, Universitas Ahmad Dahlan. All rights reserved. Universitas Ahmad Dahlan 2022 Article PeerReviewed application/pdf en https://repository.ugm.ac.id/282097/1/Ardiansyah%20et%20al%20-%202022%20-%20Optimizing%20complexity%20weight%20parameter%20of%20use%20case.pdf Ardiansyah, Ardiansyah and Ferdiana, Ridi and Permanasari, Adhistya Erna (2022) Optimizing complexity weight parameter of use case points estimation using particle swarm optimization. International Journal of Advances in Intelligent Informatics, 8 (2). 165 -184. ISSN 24426571 https://ijain.org/index.php/IJAIN/article/view/811 |
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Electrical and Electronic Engineering not elsewhere classified Ardiansyah, Ardiansyah Ferdiana, Ridi Permanasari, Adhistya Erna Optimizing complexity weight parameter of use case points estimation using particle swarm optimization |
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Among algorithmic-based frameworks for software development effort estimation, Use Case Points I s one of the most used. Use Case Points is a well-known estimation framework designed mainly for object-oriented projects. Use Case Points uses the use case complexity weight as its essential parameter. The parameter is calculated with the number of actors and transactions of the use case. Nevertheless, use case complexity weight is discontinuous, which can sometimes result in inaccurate measurements and abrupt classification of the use case. The objective of this work is to investigate the potential of integrating particle swarm optimization (PSO) with the Use Case Points framework. The optimizer algorithm is utilized to optimize the modified use case complexity weight parameter. We designed and conducted an experiment based on real-life data set from three software houses. The proposed model’s accuracy and performance evaluation metric is compared with other published results, which are standardized accuracy, effect size, mean balanced residual error, mean inverted balanced residual error, and mean absolute error. Moreover, the existing models as the benchmark are polynomial regression, multiple linear regression, weighted case-based reasoning with (PSO), fuzzy use case points, and standard Use Case Points. Experimental results show that the proposed model generates the best value of standardized accuracy of 99.27 and an effect size of 1.15 over the benchmark models. The results of our study are promising for researchers and practitioners because the proposed model is actually estimating, not guessing, and generating meaningful estimation with statistically and practically significant. © 2022, Universitas Ahmad Dahlan. All rights reserved. |
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Article PeerReviewed |
author |
Ardiansyah, Ardiansyah Ferdiana, Ridi Permanasari, Adhistya Erna |
author_facet |
Ardiansyah, Ardiansyah Ferdiana, Ridi Permanasari, Adhistya Erna |
author_sort |
Ardiansyah, Ardiansyah |
title |
Optimizing complexity weight parameter of use case points estimation using particle swarm optimization |
title_short |
Optimizing complexity weight parameter of use case points estimation using particle swarm optimization |
title_full |
Optimizing complexity weight parameter of use case points estimation using particle swarm optimization |
title_fullStr |
Optimizing complexity weight parameter of use case points estimation using particle swarm optimization |
title_full_unstemmed |
Optimizing complexity weight parameter of use case points estimation using particle swarm optimization |
title_sort |
optimizing complexity weight parameter of use case points estimation using particle swarm optimization |
publisher |
Universitas Ahmad Dahlan |
publishDate |
2022 |
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https://repository.ugm.ac.id/282097/1/Ardiansyah%20et%20al%20-%202022%20-%20Optimizing%20complexity%20weight%20parameter%20of%20use%20case.pdf https://repository.ugm.ac.id/282097/ https://ijain.org/index.php/IJAIN/article/view/811 |
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