Fine-Grained Characterization of Faults Causing Long Latency Crashes in Programs
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
As the rate of transient hardware faults increases, researchers have investigated software techniques to tolerate these faults. An important class of faults are those that cause long- latency crashes (LLCs), or faults that can persist for a long time in the program before causing it to crash. In this paper, we develop a technique to automatically find program locations where LLC causing faults originate so that the locations can be protected to bound the program's crash latency. We first identify program code patterns that are responsible for the majority of LLC causing faults through an empirical study. We then build CRASHFINDER, a tool that finds LLC locations by statically searching the program for the patterns, and then refining the static analysis results with a dynamic analysis and selective fault injection-based approach. We find that CRASHFINDER can achieve an average of 9.29 orders of magnitude time reduction to identify more than 90% of LLC causing locations in the program, compared to exhaustive fault injection techniques, and has no false-positives.
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 | 0.000 | 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