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arXiv · 2608.23259

TianoForge: An Automated Bug Triage Approach for the TianoCore UEFI Firmware Development Community

Nazanin Siavash·Terrance E. Boult·Armin Moin
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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.

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Nazanin Siavash, Terrance E. Boult, Armin Moin. 2026-09-08. TianoForge: An Automated Bug Triage Approach for the TianoCore UEFI Firmware Development Community. https://arxiv.org/abs/2608.23259

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