A Tale of Two Injectors: End-to-End Comparison of IR-Level and Assembly-Level Fault Injection
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 experimental technique to evaluate the resilience of software techniques for tolerating hardware faults. Software-implemented FI can be performed at different levels of abstraction in the system stack; FI performed at the compiler's intermediate representation (IR) level has the advantage that it is closer to the program being evaluated and is hence easier to derive insights from for the design of software fault-tolerance mechanisms. Unfortunately, it is not clear how accurate IR-level FI is vis-a-vis FI performed at the assembly code level, and prior work has presented contradictory findings. In this paper, we perform an analysis of said prior work, find an inconsistency in the FI methodology used in one study, and show that it results in a flawed comparison between IR-level and assembly-level FI. We further confirm this finding by performing a comprehensive evaluation of the accuracy of IR-level FI across a range of benchmark programs and compiler optimization levels. Our results show that IR-level FI is as accurate as assembly-level FI for silent data corruptions (SDCs) across different benchmarks and optimization levels.
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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 | 0.000 | 0.000 |
| 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