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

Abstract

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.