Optimization of COCOMO model using particle swarm optimization
Software effort and cost estimation are crucial parts of software project development. It determines the budget, time, and resources needed to develop a software project. The success of a software project development depends mainly on the accuracy of software effort and cost estimation. A p...
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my.iium.irep.914262021-08-27T00:15:27Z http://irep.iium.edu.my/91426/ Optimization of COCOMO model using particle swarm optimization Zakaria, Noor Azura Ismail, Amelia Ritahani Zainal Abidin, Nadzurah Mohd Khalid, Nur Hidayah Yakath Ali, Afrujaan QA75 Electronic computers. Computer science Software effort and cost estimation are crucial parts of software project development. It determines the budget, time, and resources needed to develop a software project. The success of a software project development depends mainly on the accuracy of software effort and cost estimation. A poor estimation will impact the result, which worsens the project management. Various software effort estimation model has been introduced to resolve this problem. COnstructive COst MOdel (COCOMO) is a well-established software project estimation model; however, it lacks accuracy in effort and cost estimation, especially for current projects. Inaccuracy and complexity in the estimated effort have made it difficult to efficiently and effectively develop software, affecting the schedule, cost, and uncertain estimation directly. In this paper, Particle Swarm Optimization (PSO) is proposed as a metaheuristics optimization method to hybrid with three traditional state-of-art techniques such as Support Vector Machine (SVM), LinearRegression (LR), and Random Forest (RF) for optimizing the parameters of COCOMO models. The proposed approach is applied to the NASA software project dataset downloaded from the promise repository. The proposed approach has been compared with the three traditional algorithms; however, the obtained results confirm low accuracy before hybridizingwith PSO. Overall, the results showed that PSOSVM on the NASA software project dataset could improve effort estimation accuracy and outperform other models. Universitas Ahmad Dahlan 2021-04-24 Article PeerReviewed application/pdf en http://irep.iium.edu.my/91426/13/91426_Optimization%20of%20COCOMO%20model%20using%20particle%20swarm%20optimization.pdf application/pdf en http://irep.iium.edu.my/91426/19/91426_Optimization%20of%20COCOMO%20model%20using%20particle%20swarm%20optimization_Scopus.pdf Zakaria, Noor Azura and Ismail, Amelia Ritahani and Zainal Abidin, Nadzurah and Mohd Khalid, Nur Hidayah and Yakath Ali, Afrujaan (2021) Optimization of COCOMO model using particle swarm optimization. International Journal of Advances in Intelligent Informatics, 7 (2). pp. 177-187. ISSN 2442-6571 E-ISSN 2548-3161 https://ijain.org/index.php/IJAIN/issue/view/20 10.26555/ijain.v7i2.583 |
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QA75 Electronic computers. Computer science Zakaria, Noor Azura Ismail, Amelia Ritahani Zainal Abidin, Nadzurah Mohd Khalid, Nur Hidayah Yakath Ali, Afrujaan Optimization of COCOMO model using particle swarm optimization |
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Software effort and cost estimation are crucial parts of software project development. It determines the budget, time, and resources needed to develop a software project. The success of a software project development depends mainly on the accuracy of software effort and cost estimation. A poor estimation will impact the result, which worsens the project management. Various software effort estimation model has been introduced to resolve this problem. COnstructive COst MOdel (COCOMO) is a well-established software project estimation model; however, it lacks accuracy in effort and cost estimation, especially for current projects. Inaccuracy and complexity in the estimated effort have made it difficult to efficiently and effectively develop software, affecting the schedule, cost, and uncertain estimation directly. In this paper, Particle Swarm Optimization (PSO) is proposed as a metaheuristics optimization method to hybrid with three traditional state-of-art techniques such as Support Vector Machine (SVM), LinearRegression (LR), and Random Forest (RF) for optimizing the parameters of COCOMO models. The proposed approach is applied to the NASA software project dataset downloaded from the promise repository. The proposed approach has been compared with the three traditional algorithms; however, the obtained results confirm low accuracy before hybridizingwith PSO. Overall, the results showed that PSOSVM on the NASA software project dataset could improve effort estimation accuracy and outperform other models. |
format |
Article |
author |
Zakaria, Noor Azura Ismail, Amelia Ritahani Zainal Abidin, Nadzurah Mohd Khalid, Nur Hidayah Yakath Ali, Afrujaan |
author_facet |
Zakaria, Noor Azura Ismail, Amelia Ritahani Zainal Abidin, Nadzurah Mohd Khalid, Nur Hidayah Yakath Ali, Afrujaan |
author_sort |
Zakaria, Noor Azura |
title |
Optimization of COCOMO model using particle swarm optimization |
title_short |
Optimization of COCOMO model using particle swarm optimization |
title_full |
Optimization of COCOMO model using particle swarm optimization |
title_fullStr |
Optimization of COCOMO model using particle swarm optimization |
title_full_unstemmed |
Optimization of COCOMO model using particle swarm optimization |
title_sort |
optimization of cocomo model using particle swarm optimization |
publisher |
Universitas Ahmad Dahlan |
publishDate |
2021 |
url |
http://irep.iium.edu.my/91426/13/91426_Optimization%20of%20COCOMO%20model%20using%20particle%20swarm%20optimization.pdf http://irep.iium.edu.my/91426/19/91426_Optimization%20of%20COCOMO%20model%20using%20particle%20swarm%20optimization_Scopus.pdf http://irep.iium.edu.my/91426/ https://ijain.org/index.php/IJAIN/issue/view/20 |
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