Hierarchical Timed Abstract State Machines for WCET Estimation
Bibliographic record
Abstract
In this paper we present an extension of the Abstract State Machines suited for the modelling of complex processors in the context of system verification. Hard real-time systems use evermore complex processors as their certification guidelines are getting tighter and more explicit regarding the verification of software. Besides processor simulation, the goal of our model is to provide a base for worst-case execution time estimation, providing abstraction capabilities that enable the scaling of analysis. The main difference between our model and other ASM extensions is that we define time as a mean to enable time accurate runs and hierarchical abstraction levels of components that can be dynamically chosen during the execution while staying the closest possible to the original ASM mathematical foundation. The model is also designed to choose a suited component definition in order to adapt to information precision on data values. The time extension helps modelling non-instantaneous actions which is essential for real-time systems. Adaptable precision and separation of the analysis from the model of the processor, will proove well suited for integration into a worst-case execution time estimation tool.
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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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".