Model-based WCET Analysis with Invariants
Bibliographic record
Abstract
The integration of worst case execution time (WCET) analysis in model-based designs allows timing problems to be discovered in the early phases of development, when they are less expensive to correct than in later phases. In this paper, we show how model-based WCET analysis can improve timing calculations compared to program-based WCET analysis. The models are described by hierarchical state machines with concurrency, probabilistic transition, stochastic transitions, costs/rewards attached to states and transitions, and invariants attached to states. In these models, user-specified invariants serve to check the correctness of designs by restricting allowed state configurations. Our contribution is to use invariants additionally to determine transition combinations (paths) that can be eliminated from the WCET analysis, with the help of a decision procedure, thus making the analysis more precise. The assembly code of transitions for a specific target is generated and execution time for that code calculated. From the model, a probabilistic timed automaton (PTA) or Markov decision process (MDP) can be created. On that model, execution times of transitions are calculated as costs.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".