Schedulability Analysis of Periodic Tasks Implementing Synchronous Finite State Machines
Why this work is in the frame
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Bibliographic record
Abstract
Model-based design of embedded systems using Synchronous Reactive (SR) models is among the best practices for software development in the automotive and aeronautics industry. The correct implementation of an SR model must guarantee the synchronous assumption, that is, all the system reactions complete before the next event. This assumption can be verified using schedulability analysis, but the analysis can be quite challenging when the system also consists of blocks implementing finite state machines, as in modern modeling tools like Simulink and SCADE. In this paper, we discuss the schedulability analysis of such systems, including the applicability of traditional task analysis methods and an algorithmic solution to compute the exact demand and request bound functions. In addition, we define conditions for computing these functions using a periodic recurrent term, even when there is no cyclic recurrent behavior in the model.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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