Factor Analysis and Random Forest Based Model of Software Cost Estimation

Wei Zhang; Haixin Cheng; Siyu Zhan; Ming Luo; Feng Wang; Zhan Huang

doi:10.2991/978-94-6463-262-0_73

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Factor Analysis and Random Forest Based Model of Software Cost Estimation

Authors

Wei Zhang¹, Haixin Cheng²^{, *}, Siyu Zhan², Ming Luo¹, Feng Wang¹, Zhan Huang¹

¹PetroChina Southwest Oil and Gasfield Company, Chengdu, 610041, China

²University of Electronic Science and Technology of China, Chengdu, 611731, China

^*Corresponding author. Email: xhcmail@std.uestc.edu.cn

Corresponding Author

Haixin Cheng

Available Online 9 October 2023.

DOI: 10.2991/978-94-6463-262-0_73 How to use a DOI?
Keywords: Software Cost Estimation; Factor Analysis; Random Forest
Abstract: Software Cost Estimation is one of the challenges in software engineering. Accurate estimates can increase the speed of the effort for developing software projects, and prevent probabilistic failure consequently. Based on factor analysis and random forest, this article proposed a new SCE model. The model recombines factors that affect software workload into six factors, measuring the size of workload from aspects such as software performance requirements, developer capabilities, and data size. The random forest model using the XGBoost framework is built to complete the software workload prediction task. Then, we evaluated the performance of the model on three datasets, including COCOMO81, and the results showed that the model has high prediction accuracy and strong robustness, and can achieve high precision with fewer data samples.
Copyright: © 2024 The Author(s)
Open Access: Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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Volume Title: Proceedings of the 3rd International Conference on Management Science and Software Engineering (ICMSSE 2023)
Series: Atlantis Highlights in Engineering
Publication Date: 9 October 2023
ISBN: 10.2991/978-94-6463-262-0_73
ISSN: 2589-4943
DOI: 10.2991/978-94-6463-262-0_73 How to use a DOI?
Open Access: Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Cite this article

ris enw bib

TY  - CONF
AU  - Wei Zhang
AU  - Haixin Cheng
AU  - Siyu Zhan
AU  - Ming Luo
AU  - Feng Wang
AU  - Zhan Huang
PY  - 2023
DA  - 2023/10/09
TI  - Factor Analysis and Random Forest Based Model of Software Cost Estimation
BT  - Proceedings of the 3rd International Conference on Management Science and Software Engineering (ICMSSE 2023)
PB  - Atlantis Press
SP  - 700
EP  - 707
SN  - 2589-4943
UR  - https://doi.org/10.2991/978-94-6463-262-0_73
DO  - 10.2991/978-94-6463-262-0_73
ID  - Zhang2023
ER  -

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