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

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.