Enhancing Agile Story Point Estimation: Integrating Deep Learning, Machine Learning, and Natural Language Processing with SBERT and Gradient Boosted Trees

Abstract

Advances in software engineering, particularly in Agile software development (ASD), demand innovative approaches to effort estimation due to the volatility in Agile environments. Recent trends have made the automation of story point (SP) estimation increasingly relevant, with significant potential for enhancing accuracy. This study introduces a novel model for software effort estimation (SEE) utilizing a deep learning (DL)-based sentence-BERT (SBERT) model for feature extraction combined with advanced gradient-boosted tree (GBT) algorithms. A comprehensive evaluation shows that the proposed model outperforms standard SEE and state-of-the-art models, demonstrating a mean absolute error (MAE) of 2.15 and a median absolute error (MdAE) of 1.85, representing a 12% improvement over the baseline model and an 18% improvement over the best-performing state-of-the-art model. The standardized accuracy (SA) is 93%, which is 7% higher than the next best model, across a dataset of 31,960 issues from 26 open-source Agile projects. This study contributes to software engineering by offering a more accurate and reliable decision support system for estimating project efforts.