Event Monitor Validation in High-Integrity Systems
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
Platforms for modern embedded systems equip an increasing number of high-performance features to provide the required levels of performance. Timing analysis solutions handle the complexity of these platforms by relying on hardware event monitors (HEMs) that provide insightful information about resource utilization and, hence, contention among tasks. As a result, HEMs have become a key element to warrant a safe timing behavior of a system, for which reason they must be validated. While some initial works target HEMs validation, they consider one HEM at a time and focus on those HEMs for which an expert can establish an expected value for relatively small code snippets. In this paper, we propose a methodology for the validation of those HEMs for which a specific expected value cannot be established a priori even for simple cases and, instead, needs to be validated in conjunction with other HEMs. Our method also deals with the natural variability of the HEMs' values in high-performance platforms when collected in different experiments. We illustrate the effectiveness of our proposed technique for validating HEMs related to cache coherence in a relevant platform in the avionics domain.
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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 | 0.001 | 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 | 0.001 | 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