Seojin Choi  Geunseok Yang

10.5626/JOK.2026.53.3.217

automatic bug report generation large language models QLoRA-4bit CTQRS Chain-of-Thought few-shot generation

Bug reports are crucial for tracking defects and maintaining software. However, in open-source environments, they are often created by non-experts, which can result in incomplete, inconsistent and less reproducible reports. Previous studies have primarily focused on template-based methods or simple fine-tuning, without fully utilizing multidimensional quality metrics like CTQRS or systematically assessing the effectiveness of few-shot prompting. This paper proposes a novel approach that integrates QLoRA-4bit fine-tuning of large language models with CTQRS-based structured prompting, Chain-of-Thought reasoning, and one or two-shot examples. Experiments conducted on a Bugzilla dataset of 3,966 pairs demonstrated significant improvements: CTQRS increased from 77% to 94%, ROUGE-1 Recall rose from 0.61 to 0.87, and SBERT similarity improved from 85 to 90. Additionally, QLoRA alone outperformed the baseline, with the supplementary strategies contributing complementary gains. These findings empirically validate that structured prompting, reasoning guidance, and minimal example provision are critical factors in enhancing performance, highlighting the practical potential of resource-efficient fine-tuning for open-source software maintenance.