Fuzzy C-mean missing data imputation for analogy-based effort estimation
The accuracy of effort estimation in one of the major factors in the success or failure of software projects. Analogy-Based Estimation (ABE) is a widely accepted estimation model since its flow human nature in selecting analogies similar in nature to the target project. The accuracy of prediction in...
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my.utm.949812022-04-29T22:32:35Z http://eprints.utm.my/id/eprint/94981/ Fuzzy C-mean missing data imputation for analogy-based effort estimation Al Mutlaq, Ayman Jalal Abang Jawawi, Dayang Norhayati Arbain, Adila Firdaus QA75 Electronic computers. Computer science The accuracy of effort estimation in one of the major factors in the success or failure of software projects. Analogy-Based Estimation (ABE) is a widely accepted estimation model since its flow human nature in selecting analogies similar in nature to the target project. The accuracy of prediction in ABE model in strongly associated with the quality of the dataset since it depends on previous completed projects for estimation. Missing Data (MD) is one of major challenges in software engineering datasets. Several missing data imputation techniques have been investigated by researchers in ABE model. Identification of the most similar donor values from the completed software projects dataset for imputation is a challenging issue in existing missing data techniques adopted for ABE model. In this study, Fuzzy C-Mean Imputation (FCMI), Mean Imputation (MI) and K-Nearest Neighbor Imputation (KNNI) are investigated to impute missing values in Desharnais dataset under different missing data percentages (Desh-Miss1, Desh-Miss2) for ABE model. FCMI-ABE technique is proposed in this study. Evaluation comparison among MI, KNNI, and (ABE-FCMI) is conducted for ABE model to identify the suitable MD imputation method. The results suggest that the use of (ABE-FCMI), rather than MI and KNNI, imputes more reliable values to incomplete software projects in the missing datasets. It was also found that the proposed imputation method significantly improves software development effort prediction of ABE model. Science and Information Organization 2021-08 Article PeerReviewed application/pdf en http://eprints.utm.my/id/eprint/94981/1/DayangNorhayati2021_FuzzyCmeanMissingDataImputation.pdf Al Mutlaq, Ayman Jalal and Abang Jawawi, Dayang Norhayati and Arbain, Adila Firdaus (2021) Fuzzy C-mean missing data imputation for analogy-based effort estimation. International Journal of Advanced Computer Science and Applications, 12 (8). pp. 628-640. ISSN 2158-107X http://dx.doi.org/10.14569/IJACSA.2021.0120874 DOI:10.14569/IJACSA.2021.0120874 |
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QA75 Electronic computers. Computer science Al Mutlaq, Ayman Jalal Abang Jawawi, Dayang Norhayati Arbain, Adila Firdaus Fuzzy C-mean missing data imputation for analogy-based effort estimation |
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The accuracy of effort estimation in one of the major factors in the success or failure of software projects. Analogy-Based Estimation (ABE) is a widely accepted estimation model since its flow human nature in selecting analogies similar in nature to the target project. The accuracy of prediction in ABE model in strongly associated with the quality of the dataset since it depends on previous completed projects for estimation. Missing Data (MD) is one of major challenges in software engineering datasets. Several missing data imputation techniques have been investigated by researchers in ABE model. Identification of the most similar donor values from the completed software projects dataset for imputation is a challenging issue in existing missing data techniques adopted for ABE model. In this study, Fuzzy C-Mean Imputation (FCMI), Mean Imputation (MI) and K-Nearest Neighbor Imputation (KNNI) are investigated to impute missing values in Desharnais dataset under different missing data percentages (Desh-Miss1, Desh-Miss2) for ABE model. FCMI-ABE technique is proposed in this study. Evaluation comparison among MI, KNNI, and (ABE-FCMI) is conducted for ABE model to identify the suitable MD imputation method. The results suggest that the use of (ABE-FCMI), rather than MI and KNNI, imputes more reliable values to incomplete software projects in the missing datasets. It was also found that the proposed imputation method significantly improves software development effort prediction of ABE model. |
format |
Article |
author |
Al Mutlaq, Ayman Jalal Abang Jawawi, Dayang Norhayati Arbain, Adila Firdaus |
author_facet |
Al Mutlaq, Ayman Jalal Abang Jawawi, Dayang Norhayati Arbain, Adila Firdaus |
author_sort |
Al Mutlaq, Ayman Jalal |
title |
Fuzzy C-mean missing data imputation for analogy-based effort estimation |
title_short |
Fuzzy C-mean missing data imputation for analogy-based effort estimation |
title_full |
Fuzzy C-mean missing data imputation for analogy-based effort estimation |
title_fullStr |
Fuzzy C-mean missing data imputation for analogy-based effort estimation |
title_full_unstemmed |
Fuzzy C-mean missing data imputation for analogy-based effort estimation |
title_sort |
fuzzy c-mean missing data imputation for analogy-based effort estimation |
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Science and Information Organization |
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2021 |
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http://eprints.utm.my/id/eprint/94981/1/DayangNorhayati2021_FuzzyCmeanMissingDataImputation.pdf http://eprints.utm.my/id/eprint/94981/ http://dx.doi.org/10.14569/IJACSA.2021.0120874 |
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