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

Shahida Hayat; Wajahat Akbar; Tariq Hussain; Muhammad Inam Ul Haq; Altaf Hussian; Irshad Khalil; Muhammad Nawaz Khan; Samsonova Diana

doi:10.62762/TIS.2024.751418

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Volume 1, Issue 3, ICCK Transactions on Intelligent Systematics

Volume 1, Issue 3, 2024

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

Free to Read | Research Article | 12 November 2024

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

Shahida Hayat 1

Wajahat Akbar 2

Tariq Hussain 3,4 *

Muhammad Inam Ul Haq 5

Altaf Hussian 5

Irshad Khalil 6,7

Muhammad Nawaz Khan 8

Samsonova Diana 9 *

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 Authors: Tariq Hussain, [email protected] ; Samsonova Diana, [email protected]

DOI: 10.62762/TIS.2024.751418

ARK: ark:/57805/tis.2024.751418

Received: 01 October 2024, Accepted: 08 November 2024, Published: 12 November 2024

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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 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

Data Availability Statement

Data will be made available on request.

Funding

This work was supported without any funding.

Conflicts of Interest

The authors declare no conflicts of interest.

Ethical Approval and Consent to Participate

Not applicable.

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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. ICCK Transactions on Intelligent Systematics, 1(3), 190-202. https://doi.org/10.62762/TIS.2024.751418

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TY  - JOUR
AU  - Hayat, Shahida
AU  - Akbar, Wajahat
AU  - Hussain, Tariq
AU  - Haq, Muhammad Inam Ul
AU  - Hussian, Altaf
AU  - Khalil, Irshad
AU  - Khan, Muhammad Nawaz
AU  - Diana, Samsonova
PY  - 2024
DA  - 2024/11/12
TI  - Improving Effort Estimation Accuracy in Software Development Projects Using Multiple Imputation Techniques for Missing Data Handling
JO  - ICCK Transactions on Intelligent Systematics
T2  - ICCK Transactions on Intelligent Systematics
JF  - ICCK Transactions on Intelligent Systematics
VL  - 1
IS  - 3
SP  - 190
EP  - 202
DO  - 10.62762/TIS.2024.751418
UR  - https://www.icck.org/article/abs/TIS.2024.751418
KW  - analogy-based effort estimation
KW  - multiple imputation
KW  - software development effort estimation
AB  - 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 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.
SN  - 3068-5079
PB  - Institute of Central Computation and Knowledge
LA  - English
ER  -

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@article{Hayat2024Improving,
  author = {Shahida Hayat and Wajahat Akbar and Tariq Hussain and Muhammad Inam Ul Haq and Altaf Hussian and Irshad Khalil and Muhammad Nawaz Khan and Samsonova Diana},
  title = {Improving Effort Estimation Accuracy in Software Development Projects Using Multiple Imputation Techniques for Missing Data Handling},
  journal = {ICCK Transactions on Intelligent Systematics},
  year = {2024},
  volume = {1},
  number = {3},
  pages = {190-202},
  doi = {10.62762/TIS.2024.751418},
  url = {https://www.icck.org/article/abs/TIS.2024.751418},
  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 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.},
  keywords = {analogy-based effort estimation, multiple imputation, software development effort estimation},
  issn = {3068-5079},
  publisher = {Institute of Central Computation and Knowledge}
}

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