{"ID":2877344,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2508.21156","arxiv_id":"2508.21156","title":"Automated Bug Triaging using Instruction-Tuned Large Language Models","abstract":"Bug triaging, the task of assigning new issues to developers, is often slow and inconsistent in large projects. We present a lightweight framework that instruction-tuned large language model (LLM) with LoRA adapters and uses candidate-constrained decoding to ensure valid assignments. Tested on EclipseJDT and Mozilla datasets, the model achieves strong shortlist quality (Hit at 10 up to 0.753) despite modest exact Top-1 accuracy. On recent snapshots, accuracy rises sharply, showing the framework's potential for real-world, human-in-the-loop triaging. Our results suggest that instruction-tuned LLMs offer a practical alternative to costly feature engineering and graph-based methods.","short_abstract":"Bug triaging, the task of assigning new issues to developers, is often slow and inconsistent in large projects. We present a lightweight framework that instruction-tuned large language model (LLM) with LoRA adapters and uses candidate-constrained decoding to ensure valid assignments. Tested on EclipseJDT and Mozilla da...","url_abs":"https://arxiv.org/abs/2508.21156","url_pdf":"https://arxiv.org/pdf/2508.21156v1","authors":"[\"Kiana Kiashemshaki\",\"Arsham Khosravani\",\"Alireza Hosseinpour\",\"Arshia Akhavan\"]","published":"2025-08-28T18:46:37Z","proceeding":"cs.SE","tasks":"[\"cs.SE\"]","methods":"[\"Large Language Model\",\"Language Model\",\"LoRA\"]","has_code":false}