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

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.