ProGem: A Hybrid AI Framework for Task Effort Estimation

Accurate effort estimation at the task level is essential for effective project planning, resource allocation, and meeting delivery timelines in software development. Traditional approaches have focused primarily on project-level estimation, leaving a critical gap in predicting the duration of individual tasks. This study presents ProGem, a novel hybrid framework that combines Google’s Gemini API with Facebook’s Prophet time-series forecasting model to estimate task effort at fine granularity. ProGem encodes contextual task features — including sentiment, priority, and urgency; and integrates temporal dynamics with semantic task understanding to produce robust duration predictions. The proposed approach is validated on 1,197 real-world tasks collected from software development environments spanning 2019 to 2025. Experimental results demonstrate that ProGem consistently outperforms both traditional models (Decision Tree, Random Forest, XGBoost) and other proposed hybrid models (RF-KNN, XGBERT), achieving the lowest MAE of 63.75, MSE of 9,987.54, RMSE of 100.45, and the highest coefficient of determination (R2 = 0.4750). On individual real-world tasks, ProGem produced estimates of 9.16, 3.00, 6.08, 4.10, and 2.25 days against actual durations of approximately 7, 3, 5–6, 4, and 2 days, respectively, reflecting a prediction accuracy in the range of 90–95%. This work bridges the gap between high-level project estimation and fine-grained task-level forecasting, offering a data-driven solution to support dynamic planning in agile and DevOps development environments.

@article{Islam2026,
  title     = {ProGem: A Hybrid AI Framework for Task Effort Estimation},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {17},
  number    = {3},
  year      = {2026},
  publisher = {The Science and Information Organization},
  author    = {Shahid Islam and Shazia Arshad and Natasha Nigar and Jose Lukose},
  doi       = {10.14569/IJACSA.2026.0170393},
  url       = {https://doi.org/10.14569/IJACSA.2026.0170393}
}