Automated Bug Triaging using Instruction-Tuned Large Language Models
Fuente:
arXiv
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866916924869312512 |
|---|---|
| author | Kiashemshaki, Kiana Khosravani, Arsham Hosseinpour, Alireza Akhavan, Arshia |
| author_facet | Kiashemshaki, Kiana Khosravani, Arsham Hosseinpour, Alireza Akhavan, Arshia |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_21156 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Automated Bug Triaging using Instruction-Tuned Large Language Models Kiashemshaki, Kiana Khosravani, Arsham Hosseinpour, Alireza Akhavan, Arshia Software Engineering D.2.7; I.2.7; I.2.6 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. |
| title | Automated Bug Triaging using Instruction-Tuned Large Language Models |
| topic | Software Engineering D.2.7; I.2.7; I.2.6 |
| url | https://arxiv.org/abs/2508.21156 |