EGPT-SPE: story point effort estimation using improved GPT-2 by removing inefficient attention heads

Estimating story points from user requirements is crucial in the Software Development Life Cycle (SDLC) as it impacts resource allocation and timelines; inaccuracies can lead to missed deadlines and increased costs, harming a company’s reputation. While various techniques have emerged to automate this process, conventional machine learning methods often fail to understand the context of user requirements, and deep learning approaches face high computational costs. To address these issues, the Efficient GPT for Story Point Estimation (EGPT-SPE) algorithm optimizes the Multi-Head Attention module by removing inefficient heads, enhancing accuracy and reducing costs. Experiments on the Choetkiertikul dataset (23,313 issues across 16 open-source projects) and the TAWOS dataset (458,232 issues across 39 open-source projects from 12 public JIRA repositories) demonstrated a 5 to 15 percent accuracy improvement in both within-project and cross-project estimations, validating the algorithm’s effectiveness in agile story point estimation.