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...
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Autor*in:	Wei Zhang [verfasserIn] Haixin Cheng [verfasserIn] Siyu Zhan [verfasserIn] Ming Luo [verfasserIn] Feng Wang [verfasserIn] Zhan Huang [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2024

Schlagwörter:	software cost estimation Autoencoder random forest COCOMO dimensionality reduction

Übergeordnetes Werk:	In: Frontiers in Physics - Frontiers Media S.A., 2014, 12(2024)
Übergeordnetes Werk:	volume:12 ; year:2024

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.3389/fphy.2024.1324719

Katalog-ID:	DOAJ092245730

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language	English
source	In Frontiers in Physics 12(2024) volume:12 year:2024
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topic_facet	software cost estimation Autoencoder random forest COCOMO dimensionality reduction Physics
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container_title	Frontiers in Physics
authorswithroles_txt_mv	Wei Zhang @@aut@@ Haixin Cheng @@aut@@ Siyu Zhan @@aut@@ Ming Luo @@aut@@ Feng Wang @@aut@@ Zhan Huang @@aut@@
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callnumber-first	Q - Science
author	Wei Zhang
spellingShingle	Wei Zhang misc QC1-999 misc software cost estimation misc Autoencoder misc random forest misc COCOMO misc dimensionality reduction misc Physics Dimensionality reduction and machine learning based model of software cost estimation
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topic_title	QC1-999 Dimensionality reduction and machine learning based model of software cost estimation software cost estimation Autoencoder random forest COCOMO dimensionality reduction
topic	misc QC1-999 misc software cost estimation misc Autoencoder misc random forest misc COCOMO misc dimensionality reduction misc Physics
topic_unstemmed	misc QC1-999 misc software cost estimation misc Autoencoder misc random forest misc COCOMO misc dimensionality reduction misc Physics
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title	Dimensionality reduction and machine learning based model of software cost estimation
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title_full	Dimensionality reduction and machine learning based model of software cost estimation
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title_sort	dimensionality reduction and machine learning based model of software cost estimation
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title_auth	Dimensionality reduction and machine learning based model of software cost estimation
abstract	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
url	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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