FQDA-ML: A Hierarchical Machine Learning-Powered Data Quality Framework for Evaluating Agile Sprint Performance: A Case Study in Community Engagement Projects

Agile software development continuously generates operational data through sprint execution, task completion, and effort estimation. However, the quality of these data is rarely assessed before they are used for analytics and predictive modeling, particularly in non-industrial settings. Existing data quality approaches primarily focus on industrial big data contexts and provide limited guidance for evaluating agile sprint repositories. This study proposes FQDA-ML, a hierarchical machine learning-powered framework for assessing sprint data quality and analyzing its relationship with delivery performance. The framework adapts Cai and Zhu’s five-dimensional data quality model and operationalizes it through 13 measurable indicators aggregated into the Sprint Data Quality Index (SDQI). The framework was validated using 129 real sprints from 13 software development teams during one academic year, organized into two consecutive academic semesters (2024-S1 and 2024-S2), corresponding to two student cohorts involved in community engagement software projects. The results showed a mean SDQI of 0.603 ± 0.217 and a strong association between SDQI and sprint completion rate (ρ = 0.719. p < 0.001). The predictive evaluation achieved an AUC-ROC of 0.968 under standard five-fold cross-validation and 0.726 under Leave-One-Group-Out validation, highlighting the influence of team-level dependency on model generalization. The findings provide an empirically validated framework for data quality assessment and predictive software analytics in agile environments.