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  - intelligent decision support
KW  - software development
KW  - intelligent project management
AB  - Intelligent project management systems rely on high-quality historical data for accurate automated decision-making, yet missing data in software project repositories remains a persistent challenge that degrades intelligent estimation performance. This study proposes an Intelligent Decision Support Framework (IDSF) for software development effort estimation (SDEE) that integrates Multiple Imputation (MI) as a critical data quality enhancement layer within the Analogy-Based Effort Estimation (ABEE) model. The framework is evaluated on the ISBSG dataset by systematically comparing six imputation strategies. Results demonstrate that the MI-enhanced framework achieves competitive and more stable MMRE values while fully preserving dataset integrity, in contrast to traditional deletion methods that cause substantial data loss. Additionally, a theoretical analysis of Long Short-Term Memory (LSTM) networks is provided as a prospective deep learning estimator, highlighting that high-quality restored data is structurally necessary for effective LSTM training. This work contributes to intelligent systems in software engineering by establishing MI as a robust data quality module, laying a strong foundation for building more reliable AI-driven intelligent project management systems and advancing intelligent systematics research.
SN  - 3068-5079
PB  - Institute of Central Computation and Knowledge
LA  - English
ER  - 

@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 = {Intelligent project management systems rely on high-quality historical data for accurate automated decision-making, yet missing data in software project repositories remains a persistent challenge that degrades intelligent estimation performance. This study proposes an Intelligent Decision Support Framework (IDSF) for software development effort estimation (SDEE) that integrates Multiple Imputation (MI) as a critical data quality enhancement layer within the Analogy-Based Effort Estimation (ABEE) model. The framework is evaluated on the ISBSG dataset by systematically comparing six imputation strategies. Results demonstrate that the MI-enhanced framework achieves competitive and more stable MMRE values while fully preserving dataset integrity, in contrast to traditional deletion methods that cause substantial data loss. Additionally, a theoretical analysis of Long Short-Term Memory (LSTM) networks is provided as a prospective deep learning estimator, highlighting that high-quality restored data is structurally necessary for effective LSTM training. This work contributes to intelligent systems in software engineering by establishing MI as a robust data quality module, laying a strong foundation for building more reliable AI-driven intelligent project management systems and advancing intelligent systematics research.},
  keywords = {intelligent decision support, software development, intelligent project management},
  issn = {3068-5079},
  publisher = {Institute of Central Computation and Knowledge}
}