一句话：提出 LBT-P，用知识蒸馏、知识保持微调及利用浅层/深层表示的损失函数，将大型 PLM 高效用于缺陷分派。
缺陷分派旨在为缺陷报告分配合适的开发者，但人工分派耗时费力；自动分派的关键是从缺陷报告中抽取语义信息。大型预训练语言模型虽在 NLP 中表现优异，却因模型大、推理慢、微调易灾难性遗忘等问题难以直接用于缺陷分派。本文提出 LBT-P 框架：利用知识蒸馏将大型 PLM 压缩为小型快速模型，通过知识保持微调避免灾难性遗忘，并设计新的损失函数，同时利用浅层与深层表示来缓解“过思考”问题。作者在真实私有数据集以及 Google Chromium、Mozilla Core、Mozilla Firefox 三个公开数据集上评估，实验结果显示 LBT-P 优于对比方法。
Assigning appropriate developers to the bugs is one of the main challenges in bug triage. Demands for automatic bug triage are increasing in the industry, as manual bug triage is labor-intensive and time-consuming in large projects. The key to the bug triage task is extracting semantic information from a bug report. In recent years, large Pre-trained Language Models (PLMs) including BERT [4] have achieved dramatic progress in the natural language processing (NLP) domain. However, applying large PLMs to the bug triage task for extracting semantic information has several challenges. In this paper, we address the challenges and propose a novel framework for bug triage named LBT-P, standing for Light Bug Triage framework with a Pre-trained language model. It compresses a large PLM into small and fast models using knowledge distillation techniques and also prevents catastrophic forgetting of PLM by introducing knowledge preservation fine-tuning. We also develop a new loss function exploiting representations of earlier layers as well as deeper layers in order to handle the overthinking problem. We demonstrate our proposed framework on the real-world private dataset and three public real-world datasets [11]: Google Chromium, Mozilla Core, and Mozilla Firefox. The result of the experiments shows the superiority of LBT-P.
DOI 原文 ·
@inproceedings{paperbot7358,
title = {A Light Bug Triage Framework for Applying Large Pre-trained Language Model},
author = {Jaehyung Lee and Kisun Han and Hwanjo Yu},
booktitle = {ASE},
year = {2022},
doi = {10.1145/3551349.3556898}
}