Dimensionality reduction and machine learning based model of software cost estimation
Software Cost Estimation (SCE) is one of the research priorities and challenges in the construction of cyber-physical-social systems (CPSSs). In CPSS, it is urge to process environmental and social information accurately and use it to guide social practice. Thus, in response to the problems of low p...
Ausführliche Beschreibung
Autor*in: |
Wei Zhang [verfasserIn] Haixin Cheng [verfasserIn] Siyu Zhan [verfasserIn] Ming Luo [verfasserIn] Feng Wang [verfasserIn] Zhan Huang [verfasserIn] |
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E-Artikel |
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Sprache: |
Englisch |
Erschienen: |
2024 |
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Übergeordnetes Werk: |
In: Frontiers in Physics - Frontiers Media S.A., 2014, 12(2024) |
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Übergeordnetes Werk: |
volume:12 ; year:2024 |
Links: |
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DOI / URN: |
10.3389/fphy.2024.1324719 |
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Katalog-ID: |
DOAJ092245730 |
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10.3389/fphy.2024.1324719 doi (DE-627)DOAJ092245730 (DE-599)DOAJeff7b121c5d9448bb7ca914c159da948 DE-627 ger DE-627 rakwb eng QC1-999 Wei Zhang verfasserin aut Dimensionality reduction and machine learning based model of software cost estimation 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Software Cost Estimation (SCE) is one of the research priorities and challenges in the construction of cyber-physical-social systems (CPSSs). In CPSS, it is urge to process environmental and social information accurately and use it to guide social practice. Thus, in response to the problems of low prediction accuracy, poor robustness, and poor interpretability in SCE, this paper proposes a SCE model based on Autoencoder and Random Forest. First, preprocess the project data, remove outliers, and build regression trees to fill in missing attributes in the data. Second, construct a Autoencoder to reduce the dimensionality of factors that affect software cost. Subsequently, the performance of the model was trained and validated using the XGBoost framework on three datasets: COCOMO81, Albrecht, and Desharnais, and compared with common cost prediction models. The experimental results show that the MMRE, MdMRE, and PRED (0.25) values of the proposed model on the COCOMO81 dataset reached 0.21, 0.16, and 0.71, respectively. Compared with other models, the proposed model achieved significant improvements in accuracy and robustness. software cost estimation Autoencoder random forest COCOMO dimensionality reduction Physics Haixin Cheng verfasserin aut Haixin Cheng verfasserin aut Siyu Zhan verfasserin aut Siyu Zhan verfasserin aut Ming Luo verfasserin aut Feng Wang verfasserin aut Zhan Huang verfasserin aut In Frontiers in Physics Frontiers Media S.A., 2014 12(2024) (DE-627)750371749 (DE-600)2721033-9 2296424X nnns volume:12 year:2024 https://doi.org/10.3389/fphy.2024.1324719 kostenfrei https://doaj.org/article/eff7b121c5d9448bb7ca914c159da948 kostenfrei https://www.frontiersin.org/articles/10.3389/fphy.2024.1324719/full kostenfrei https://doaj.org/toc/2296-424X Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 12 2024 |
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10.3389/fphy.2024.1324719 doi (DE-627)DOAJ092245730 (DE-599)DOAJeff7b121c5d9448bb7ca914c159da948 DE-627 ger DE-627 rakwb eng QC1-999 Wei Zhang verfasserin aut Dimensionality reduction and machine learning based model of software cost estimation 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Software Cost Estimation (SCE) is one of the research priorities and challenges in the construction of cyber-physical-social systems (CPSSs). In CPSS, it is urge to process environmental and social information accurately and use it to guide social practice. Thus, in response to the problems of low prediction accuracy, poor robustness, and poor interpretability in SCE, this paper proposes a SCE model based on Autoencoder and Random Forest. First, preprocess the project data, remove outliers, and build regression trees to fill in missing attributes in the data. Second, construct a Autoencoder to reduce the dimensionality of factors that affect software cost. Subsequently, the performance of the model was trained and validated using the XGBoost framework on three datasets: COCOMO81, Albrecht, and Desharnais, and compared with common cost prediction models. The experimental results show that the MMRE, MdMRE, and PRED (0.25) values of the proposed model on the COCOMO81 dataset reached 0.21, 0.16, and 0.71, respectively. Compared with other models, the proposed model achieved significant improvements in accuracy and robustness. software cost estimation Autoencoder random forest COCOMO dimensionality reduction Physics Haixin Cheng verfasserin aut Haixin Cheng verfasserin aut Siyu Zhan verfasserin aut Siyu Zhan verfasserin aut Ming Luo verfasserin aut Feng Wang verfasserin aut Zhan Huang verfasserin aut In Frontiers in Physics Frontiers Media S.A., 2014 12(2024) (DE-627)750371749 (DE-600)2721033-9 2296424X nnns volume:12 year:2024 https://doi.org/10.3389/fphy.2024.1324719 kostenfrei https://doaj.org/article/eff7b121c5d9448bb7ca914c159da948 kostenfrei https://www.frontiersin.org/articles/10.3389/fphy.2024.1324719/full kostenfrei https://doaj.org/toc/2296-424X Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 12 2024 |
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10.3389/fphy.2024.1324719 doi (DE-627)DOAJ092245730 (DE-599)DOAJeff7b121c5d9448bb7ca914c159da948 DE-627 ger DE-627 rakwb eng QC1-999 Wei Zhang verfasserin aut Dimensionality reduction and machine learning based model of software cost estimation 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Software Cost Estimation (SCE) is one of the research priorities and challenges in the construction of cyber-physical-social systems (CPSSs). In CPSS, it is urge to process environmental and social information accurately and use it to guide social practice. Thus, in response to the problems of low prediction accuracy, poor robustness, and poor interpretability in SCE, this paper proposes a SCE model based on Autoencoder and Random Forest. First, preprocess the project data, remove outliers, and build regression trees to fill in missing attributes in the data. Second, construct a Autoencoder to reduce the dimensionality of factors that affect software cost. Subsequently, the performance of the model was trained and validated using the XGBoost framework on three datasets: COCOMO81, Albrecht, and Desharnais, and compared with common cost prediction models. The experimental results show that the MMRE, MdMRE, and PRED (0.25) values of the proposed model on the COCOMO81 dataset reached 0.21, 0.16, and 0.71, respectively. Compared with other models, the proposed model achieved significant improvements in accuracy and robustness. software cost estimation Autoencoder random forest COCOMO dimensionality reduction Physics Haixin Cheng verfasserin aut Haixin Cheng verfasserin aut Siyu Zhan verfasserin aut Siyu Zhan verfasserin aut Ming Luo verfasserin aut Feng Wang verfasserin aut Zhan Huang verfasserin aut In Frontiers in Physics Frontiers Media S.A., 2014 12(2024) (DE-627)750371749 (DE-600)2721033-9 2296424X nnns volume:12 year:2024 https://doi.org/10.3389/fphy.2024.1324719 kostenfrei https://doaj.org/article/eff7b121c5d9448bb7ca914c159da948 kostenfrei https://www.frontiersin.org/articles/10.3389/fphy.2024.1324719/full kostenfrei https://doaj.org/toc/2296-424X Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 12 2024 |
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10.3389/fphy.2024.1324719 doi (DE-627)DOAJ092245730 (DE-599)DOAJeff7b121c5d9448bb7ca914c159da948 DE-627 ger DE-627 rakwb eng QC1-999 Wei Zhang verfasserin aut Dimensionality reduction and machine learning based model of software cost estimation 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Software Cost Estimation (SCE) is one of the research priorities and challenges in the construction of cyber-physical-social systems (CPSSs). In CPSS, it is urge to process environmental and social information accurately and use it to guide social practice. Thus, in response to the problems of low prediction accuracy, poor robustness, and poor interpretability in SCE, this paper proposes a SCE model based on Autoencoder and Random Forest. First, preprocess the project data, remove outliers, and build regression trees to fill in missing attributes in the data. Second, construct a Autoencoder to reduce the dimensionality of factors that affect software cost. Subsequently, the performance of the model was trained and validated using the XGBoost framework on three datasets: COCOMO81, Albrecht, and Desharnais, and compared with common cost prediction models. The experimental results show that the MMRE, MdMRE, and PRED (0.25) values of the proposed model on the COCOMO81 dataset reached 0.21, 0.16, and 0.71, respectively. Compared with other models, the proposed model achieved significant improvements in accuracy and robustness. software cost estimation Autoencoder random forest COCOMO dimensionality reduction Physics Haixin Cheng verfasserin aut Haixin Cheng verfasserin aut Siyu Zhan verfasserin aut Siyu Zhan verfasserin aut Ming Luo verfasserin aut Feng Wang verfasserin aut Zhan Huang verfasserin aut In Frontiers in Physics Frontiers Media S.A., 2014 12(2024) (DE-627)750371749 (DE-600)2721033-9 2296424X nnns volume:12 year:2024 https://doi.org/10.3389/fphy.2024.1324719 kostenfrei https://doaj.org/article/eff7b121c5d9448bb7ca914c159da948 kostenfrei https://www.frontiersin.org/articles/10.3389/fphy.2024.1324719/full kostenfrei https://doaj.org/toc/2296-424X Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 12 2024 |
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10.3389/fphy.2024.1324719 doi (DE-627)DOAJ092245730 (DE-599)DOAJeff7b121c5d9448bb7ca914c159da948 DE-627 ger DE-627 rakwb eng QC1-999 Wei Zhang verfasserin aut Dimensionality reduction and machine learning based model of software cost estimation 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Software Cost Estimation (SCE) is one of the research priorities and challenges in the construction of cyber-physical-social systems (CPSSs). In CPSS, it is urge to process environmental and social information accurately and use it to guide social practice. Thus, in response to the problems of low prediction accuracy, poor robustness, and poor interpretability in SCE, this paper proposes a SCE model based on Autoencoder and Random Forest. First, preprocess the project data, remove outliers, and build regression trees to fill in missing attributes in the data. Second, construct a Autoencoder to reduce the dimensionality of factors that affect software cost. Subsequently, the performance of the model was trained and validated using the XGBoost framework on three datasets: COCOMO81, Albrecht, and Desharnais, and compared with common cost prediction models. The experimental results show that the MMRE, MdMRE, and PRED (0.25) values of the proposed model on the COCOMO81 dataset reached 0.21, 0.16, and 0.71, respectively. Compared with other models, the proposed model achieved significant improvements in accuracy and robustness. software cost estimation Autoencoder random forest COCOMO dimensionality reduction Physics Haixin Cheng verfasserin aut Haixin Cheng verfasserin aut Siyu Zhan verfasserin aut Siyu Zhan verfasserin aut Ming Luo verfasserin aut Feng Wang verfasserin aut Zhan Huang verfasserin aut In Frontiers in Physics Frontiers Media S.A., 2014 12(2024) (DE-627)750371749 (DE-600)2721033-9 2296424X nnns volume:12 year:2024 https://doi.org/10.3389/fphy.2024.1324719 kostenfrei https://doaj.org/article/eff7b121c5d9448bb7ca914c159da948 kostenfrei https://www.frontiersin.org/articles/10.3389/fphy.2024.1324719/full kostenfrei https://doaj.org/toc/2296-424X Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 12 2024 |
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Software Cost Estimation (SCE) is one of the research priorities and challenges in the construction of cyber-physical-social systems (CPSSs). In CPSS, it is urge to process environmental and social information accurately and use it to guide social practice. Thus, in response to the problems of low prediction accuracy, poor robustness, and poor interpretability in SCE, this paper proposes a SCE model based on Autoencoder and Random Forest. First, preprocess the project data, remove outliers, and build regression trees to fill in missing attributes in the data. Second, construct a Autoencoder to reduce the dimensionality of factors that affect software cost. Subsequently, the performance of the model was trained and validated using the XGBoost framework on three datasets: COCOMO81, Albrecht, and Desharnais, and compared with common cost prediction models. The experimental results show that the MMRE, MdMRE, and PRED (0.25) values of the proposed model on the COCOMO81 dataset reached 0.21, 0.16, and 0.71, respectively. Compared with other models, the proposed model achieved significant improvements in accuracy and robustness. |
abstractGer |
Software Cost Estimation (SCE) is one of the research priorities and challenges in the construction of cyber-physical-social systems (CPSSs). In CPSS, it is urge to process environmental and social information accurately and use it to guide social practice. Thus, in response to the problems of low prediction accuracy, poor robustness, and poor interpretability in SCE, this paper proposes a SCE model based on Autoencoder and Random Forest. First, preprocess the project data, remove outliers, and build regression trees to fill in missing attributes in the data. Second, construct a Autoencoder to reduce the dimensionality of factors that affect software cost. Subsequently, the performance of the model was trained and validated using the XGBoost framework on three datasets: COCOMO81, Albrecht, and Desharnais, and compared with common cost prediction models. The experimental results show that the MMRE, MdMRE, and PRED (0.25) values of the proposed model on the COCOMO81 dataset reached 0.21, 0.16, and 0.71, respectively. Compared with other models, the proposed model achieved significant improvements in accuracy and robustness. |
abstract_unstemmed |
Software Cost Estimation (SCE) is one of the research priorities and challenges in the construction of cyber-physical-social systems (CPSSs). In CPSS, it is urge to process environmental and social information accurately and use it to guide social practice. Thus, in response to the problems of low prediction accuracy, poor robustness, and poor interpretability in SCE, this paper proposes a SCE model based on Autoencoder and Random Forest. First, preprocess the project data, remove outliers, and build regression trees to fill in missing attributes in the data. Second, construct a Autoencoder to reduce the dimensionality of factors that affect software cost. Subsequently, the performance of the model was trained and validated using the XGBoost framework on three datasets: COCOMO81, Albrecht, and Desharnais, and compared with common cost prediction models. The experimental results show that the MMRE, MdMRE, and PRED (0.25) values of the proposed model on the COCOMO81 dataset reached 0.21, 0.16, and 0.71, respectively. Compared with other models, the proposed model achieved significant improvements in accuracy and robustness. |
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title_short |
Dimensionality reduction and machine learning based model of software cost estimation |
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https://doi.org/10.3389/fphy.2024.1324719 https://doaj.org/article/eff7b121c5d9448bb7ca914c159da948 https://www.frontiersin.org/articles/10.3389/fphy.2024.1324719/full https://doaj.org/toc/2296-424X |
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