ra4xstate: An Efficient Quantitative Robustness Analysis Approach for Statecharts
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
Robustness analysis is a part of the validation process that includes testing the behavior of a system against its specification under unexpected conditions in order to check whether the system fulfills robustness requirements or not. This paper proposes ra4xstate, a robustness analysis framework in the context of Model-Driven Development, for finite state machines. Our approach takes a behavioural and a property model of the system under the test as inputs and evaluates the robustness of the system based on a notion of the cost that is computed for every off-track execution step. The experimental results show that compared to the traditional approaches that annotate traces with timestamps and variable values ra4xstate detects almost all nonrobust instances while reducing the size of the trace significantly and incurring similar runtime overhead.
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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 | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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