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.
@article{arxiv.2508.21156,
title = {Automated Bug Triaging using Instruction-Tuned Large Language Models},
author = {Kiana Kiashemshaki and Arsham Khosravani and Alireza Hosseinpour and Arshia Akhavan},
journal= {arXiv preprint arXiv:2508.21156},
year = {2025}
}