Hierarchical Timed Abstract State Machines for WCET Estimation
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
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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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 | 0.004 | 0.001 |
| 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 | 0.001 |
| 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