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IECE Transactions on Intelligent Systematics, 2024, Volume 1, Issue 3: 190-202

Free Access | Research Article | 12 November 2024
1 Department of Computer Science, University of Peshawar, Pakistan
2 School of Electronic and Control Engineering, Chang’an University, Xián 710064, China
3 School of Computer Science and Technology, Zhejiang Gongshang University, Hangzhou 310018, China
4 School of Mathematics and Statistics, Zhejiang Gongshang University, Hangzhou 310018, China
5 Department of Computer Science and Bioinformatics, Khushal Khan Khattak University Karak, Pakistan
6 Department of Health Science and Technology, Gachon University, Incheon 21936, Republic of Korea
7 Gachon Advanced Institute for Health Sciences and Technology, Gachon University, Incheon 21936, Republic of Korea
8 Department of Computer Science and Information Technology, University of Malakand, Chakdara, Pakistan
9 School of International Education, Zhejiang Gongshang University, Hangzhou 310018, China
* Corresponding author: Samsonova Diana, email: [email protected]
Received: 01 October 2024, Accepted: 08 November 2024, Published: 12 November 2024  

Abstract
The challenge of accurately estimating effort for software development projects is critical for project managers (PM) and researchers. A common issue they encounter is missing data values in datasets, which complicates effort estimation (EE). While several models have been introduced to address this issue, none have proven entirely effective. The Analogy-Based Effort Estimation (ABEE) model is the most widely used approach, relying on historical data for estimation. However, the common practice of deleting cases or cells with missing observations results in a reduction of statistical power and negatively impacts the performance of ABEE, leading to inefficiencies and biases. This study employs the Multiple Imputation (MI) technique to address missing data by filling in incomplete cases. A comparison is conducted between the original and imputed ISBSG datasets for both small- and large-scale projects, using six other imputation techniques to identify the most effective method for ABEE. The results demonstrate that the MI technique enhances effort estimation, providing more accurate and efficient outcomes while preserving valuable information throughout the project estimation process.

Graphical Abstract
Improving Effort Estimation Accuracy in Software Development Projects Using Multiple Imputation Techniques for Missing Data Handling

Keywords
analogy-based effort estimation
multiple imputation
software development effort estimation

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Cite This Article
APA Style
Hayat, S., Akbar, W., Hussain, T., Haq, M. I. U., Hussian, A., Khalil, I., Khan, M. M., & Diana, S. (2024). Improving Effort Estimation Accuracy in Software Development Projects Using Multiple Imputation Techniques for Missing Data Handling. IECE Transactions on Intelligent Systematics, 1(3), 190-202. https://doi.org/10.62762/TIS.2024.751418

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