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<p>Bug report summarization aims to generate concise and accurate descriptions to help developers understand and maintain. The existing methodologies prioritize simplifying reporting content but fail to provide a structured and well-rounded description of bugs, limiting developers’ understanding efficiency. In this paper, we leverage large language models (LLMs) to generate detailed, multi-dimensional summaries. Our intuition is based on the following facts: (1) LLMs establish robust semantic connections through extensive pre-training on paired data; (2) Real-world bug reports contain multi-dimensional information. We propose the Bug Report Multi-Dimensional Summary (BRMDS) approach, defining five dimensions: environment, actual behavior, expected behavior, bug category, and solution suggestions, and use specific instructions for each dimension to guide LLM in Parameter Efficient Fine-Tuning (PEFT). We construct a dataset in multi-dimensional information for PEFT and experimental evaluation, thereby addressing the gaps in existing datasets within this domain. The experimental results show that multi-dimensional summaries enhance developers’ understanding of bug reports. BRMDS approach outperforms baseline approaches in both automatic and human evaluations. Our datasets are publicly available at <a href="https://github.com/yunjua/bug-reports-multi-dimensional">https://github.com/yunjua/bug-reports-multi-dimensional</a>.</p>

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BRMDS: an LLM-based multi-dimensional summary generation approach for bug reports

  • 期刊论文

  • 发表日期：

    2025年9月23日

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  • Yayun Zhang,
  • Yuying Li,
  • Minying Fang,
  • Xing Yuan,
  • Junwei Du

摘要

Bug report summarization aims to generate concise and accurate descriptions to help developers understand and maintain. The existing methodologies prioritize simplifying reporting content but fail to provide a structured and well-rounded description of bugs, limiting developers’ understanding efficiency. In this paper, we leverage large language models (LLMs) to generate detailed, multi-dimensional summaries. Our intuition is based on the following facts: (1) LLMs establish robust semantic connections through extensive pre-training on paired data; (2) Real-world bug reports contain multi-dimensional information. We propose the Bug Report Multi-Dimensional Summary (BRMDS) approach, defining five dimensions: environment, actual behavior, expected behavior, bug category, and solution suggestions, and use specific instructions for each dimension to guide LLM in Parameter Efficient Fine-Tuning (PEFT). We construct a dataset in multi-dimensional information for PEFT and experimental evaluation, thereby addressing the gaps in existing datasets within this domain. The experimental results show that multi-dimensional summaries enhance developers’ understanding of bug reports. BRMDS approach outperforms baseline approaches in both automatic and human evaluations. Our datasets are publicly available at https://github.com/yunjua/bug-reports-multi-dimensional.