Problem
Research questions and friction points this paper is trying to address.
Bug Triage
TianoCore
UEFI Firmware
Software Maintenance
Automated Bug Triage
TianoForge: An Automated Bug Triage Approach for the TianoCore UEFI Firmware Development Community
🤖 AI Summary
本文提出TianoForge方法，利用机器学习和GPT模型自动处理UEFI固件开发中的错误分类问题，大幅缩短了错误处理时间。
📝 Abstract
We propose a novel approach to bug triage in the TianoCore open-source UEFI firmware development ecosystem. This integrated approach, called TianoForge, deploys the state of the art in artificial intelligence, specifically machine learning, to enable automated bug triage. This includes invalid bug report detection, duplicate bug report detection, bug report prioritization, and bug report assignment. We use various Generative Pretrained Transformer (GPT) Large Language Models (LLMs) with and without Retrieval Augmented Generation (RAG) to automate these tasks. Given the crucial role of bug triage in software maintenance and the huge number of untriaged issues in the TianoCore community, in particular, their primary project, EDK II, we expect a significant impact on the efficiency of TianoCore software maintenance processes, primarily bug triage and resolution. Our experimental study shows that TianoForge reduces the average bug triage time from around 11 days to approximately 7 minutes, which is a 99.95% reduction.
Innovation
Methods, ideas, or system contributions that make the work stand out.
Automated Bug Triage
Machine Learning
Generative Pretrained Transformer (GPT)
Retrieval Augmented Generation (RAG)
TianoCore UEFI Firmware
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