# Automated Bug Triaging using Instruction-Tuned Large Language Models

arXiv: https://arxiv.org/abs/2508.21156 · HTML: https://mlsanity.com/paper/2508.21156/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Aug 2025.

## Built on by (most cited first, 1 of 1)

- [Automated Root-Cause Subclassification and No-Code Fix Generation for Invalid Bug Reports](https://mlsanity.com/paper/2605.17561/) (2026)

## Abstract (arXiv)

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.