Fault injection at the instruction set architecture (ISA) level
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Fault Injection (FI) is a commonly used technique to evaluate the reliability of systems. As soft errors become more commonplace in computer systems, it is often necessary to involve the software in the overall system's resilience. Therefore, it is important to inject faults at the ISA level to emulate soft errors that are visible to the software, in order to test software resilience mechanisms. Consequently, there is a need to develop Instruction Set Architecture (ISA)-level FI tools and techniques. We start by outlining the goals of ISA-level FI, followed by the main metrics that can be measured by the same. We then present a survey of techniques in the literature that attempt to inject faults at the ISA-level and up in the system stack. Finally, we present an overview of LLFI and PINFI, two fault injectors developed inour research group, that allow programers to inject faults at the LLVM compiler's Intermediate Representation (IR) level and x86 assembly code level, respectively. We conclude with a survey of the open challenges in the area.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it