Scholarlink

<p>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.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

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

  • 期刊论文

  • 发表日期：

    2025年10月2日

查看全文
  • Amna Shahid Cheemaa,
  • Muhammad Azhar,
  • Fahim Arif,
  • Qazi Mazhar ul haq,
  • Muhammad Sohail,
  • Asma Iqbal

摘要

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.