Proceedings of the 3rd International Conference on Management Science and Software Engineering (ICMSSE 2023)

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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_73How 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
978-94-6463-262-0
ISSN
2589-4943
DOI
10.2991/978-94-6463-262-0_73How to use a DOI?
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.

Cite this article

risenwbib
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  -