TianoForge: An Automated Bug Triage Approach for the TianoCore UEFI Firmware Development Community
2026-08-24 • Software Engineering
Software Engineering
AI summaryⓘ
The authors created TianoForge, a new system that uses advanced AI, especially machine learning and GPT language models, to help manage bug reports in the TianoCore firmware project. Their system can automatically find invalid or duplicate bug reports, decide which bugs are most important, and assign issues to the right people. They tested TianoForge and found it drastically speeds up bug handling, reducing the average time from 11 days to just 7 minutes. This can make maintaining the TianoCore software much faster and more efficient.
bug triageTianoCoreUEFI firmwaremachine learningGPTLarge Language ModelsRetrieval Augmented Generationbug report prioritizationsoftware maintenanceopen source
Authors
Nazanin Siavash, Terrance E. Boult, Armin Moin
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.