On the use of model checking for the verification of a dynamic signature monitoring approach
Bibliographic record
Abstract
Consequences of transient faults represent a significant problem for today's electronic circuits and systems. As the probability of such errors increases, incorporation of error detection and correction mechanisms is mandatory. It is well known that traditional techniques that validate system's reliability do not cover the whole spectrum of fault scenarios, because fault models are linked to target architectures. Therefore, validating the completeness of robust fault tolerance techniques is a major issue when assessing reliability improvements these techniques can produce. In this paper, we propose an original approach to evaluate the system reliability with respect to Single Event Upset (SEU) errors. It is based on model-checking principles. In addition, a signature analysis technique is evaluated. This technique was previously validated using a simulation-based fault injection approach. Simulation results showed that no error escapes detection. However, simulation based fault injection cannot guarantee that all fault consequences have been investigated. This limitation motivates us to explore a formal verification approach that targets a complete validation. Model checking has a fundamental advantage over classic fault-injection techniques: it can cover all possible SEU fault scenarios from a predefined class. Results reported in this paper demonstrate the efficiency of this validation approach over usual simulation-based techniques.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".