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<p>Bug reports play a critical role in the software development lifecycle by helping developers identify and resolve defects efficiently. However, the quality of bug report titles, particularly in open-source communities, can vary significantly, which complicates the bug triage and resolution processes. Existing approaches, such as iTAPE, treat title generation as a one-sentence summarization task using sequence-to-sequence models. While these methods show promise, they face two major limitations: (1) they do not consider the distinct components of bug reports, treating the entire report as a homogeneous input, and (2) they struggle to handle the variability between template-based and non-template-based reports, often resulting in suboptimal titles. To address these limitations, we propose <span>TAB</span>, a hybrid framework that combines a <i>Document Component Analyzer</i> based on a pre-trained BERT model and a <i>Title Generation Model</i> based on CodeT5. <span>TAB</span> addresses the first limitation by segmenting bug reports into four components-<i>Description</i>, <i>Reproduction</i>, <i>Expected Behavior</i>, and <i>Others</i>-to ensure better alignment between input and output. For the second limitation, <span>TAB</span> uses a divergent approach: for template-based reports, titles are generated directly, while for non-template reports, DCA extracts key components to improve title relevance and clarity. We evaluate <span>TAB</span> on both template-based and non-template-based bug reports, demonstrating that it significantly outperforms existing methods. Specifically, <span>TAB</span> achieves average improvements of 170.4–389.5% in METEOR, 67.8–190.0% in ROUGE-L, and 65.7–124.5% in chrF(AF) compared to baseline approaches on template-based reports. Additionally, on non-template-based reports, <span>TAB</span> shows an average improvement of 64% in METEOR, 3.6% in ROUGE-L, and 14.8% in chrF(AF) over the state-of-the-art. These results confirm the robustness of <span>TAB</span> in generating high-quality titles across diverse bug report formats.</p>

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Tab: template-aware bug report title generation via two-phase fine-tuned models

  • 期刊论文

  • 发表日期：

    2025年3月22日

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  • Xiao Liu,
  • Yinkang Xu,
  • Weifeng Sun,
  • Naiqi Huang,
  • Song Sun,
  • Qiang Li,
  • Dan Yang,
  • Meng Yan

摘要

Bug reports play a critical role in the software development lifecycle by helping developers identify and resolve defects efficiently. However, the quality of bug report titles, particularly in open-source communities, can vary significantly, which complicates the bug triage and resolution processes. Existing approaches, such as iTAPE, treat title generation as a one-sentence summarization task using sequence-to-sequence models. While these methods show promise, they face two major limitations: (1) they do not consider the distinct components of bug reports, treating the entire report as a homogeneous input, and (2) they struggle to handle the variability between template-based and non-template-based reports, often resulting in suboptimal titles. To address these limitations, we propose TAB, a hybrid framework that combines a Document Component Analyzer based on a pre-trained BERT model and a Title Generation Model based on CodeT5. TAB addresses the first limitation by segmenting bug reports into four components-Description, Reproduction, Expected Behavior, and Others-to ensure better alignment between input and output. For the second limitation, TAB uses a divergent approach: for template-based reports, titles are generated directly, while for non-template reports, DCA extracts key components to improve title relevance and clarity. We evaluate TAB on both template-based and non-template-based bug reports, demonstrating that it significantly outperforms existing methods. Specifically, TAB achieves average improvements of 170.4–389.5% in METEOR, 67.8–190.0% in ROUGE-L, and 65.7–124.5% in chrF(AF) compared to baseline approaches on template-based reports. Additionally, on non-template-based reports, TAB shows an average improvement of 64% in METEOR, 3.6% in ROUGE-L, and 14.8% in chrF(AF) over the state-of-the-art. These results confirm the robustness of TAB in generating high-quality titles across diverse bug report formats.