TianoForge：面向TianoCore UEFI固件开发社区的自动化Bug分类方法
TianoForge: An Automated Bug Triage Approach for the TianoCore UEFI Firmware Development Community
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中文总结 AI 辅助
针对TianoCore UEFI固件开发社区，提出基于GPT-LLM（结合或不结合RAG）的自动化Bug分类方法TianoForge，可将平均Bug分类时间缩短99.95%，显著提升软件维护效率。
中文摘要 AI 辅助
我们提出了一种针对TianoCore开源UEFI固件开发生态系统中Bug分类的新方法，该集成方法名为TianoForge，采用人工智能领域的最新技术，特别是机器学习，以实现自动化Bug分类，包括无效Bug报告检测、重复Bug报告检测、Bug报告优先级排序以及Bug报告分配。我们使用各种生成式预训练Transformer（GPT）大语言模型（LLM），结合或不结合检索增强生成（RAG）来自动化这些任务。鉴于Bug分类在软件维护中的关键作用，以及TianoCore社区（尤其是其主要项目EDK II）中存在大量未分类问题，我们预计这将对TianoCore软件维护流程的效率产生重大影响，主要涉及Bug分类和Bug解决。我们的实验研究表明，TianoForge将平均Bug分类时间从约11天缩短至约7分钟，降幅达99.95%。
英文摘要
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.
