A testing framework for DEVS formalism implementations
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Bibliographic record
Abstract
trace representation The Discrete-Event system Specification (DEVS) is a widely used formalism for discrete-event modelling and simulation. A variety of DEVS modelling and simulation tools have been implemented. Diverse implementations with platformspecific characteristics and often tailored to specific problem domains need to be tested to ensure their compliance with the precise and formal DEVS formalism specification. Such compliance allows for meaningful exchange and re-use of models. It also allows for the correct comparison of simulator implementation performance and hence of specific implementation optimizations. In this paper, we focus on testing “correctness” and “preciseness ” of DEVS implementations and propose a testing framework. Our testing framework combines black-box and white-box testing approaches. We start with the proposal of a standard XML representation for eventand state-traces (also known as segments). We then systematically derive a suite of concrete test cases covering all possible DEVS constructs and their combinations. We apply our testing framework to PythonDEVS and DEVS++, two concrete implementations of the Classic DEVS formalism. Analysis of the test results reveals candidate items for improvement of the two tools. Finally, insights gained into DEVS standardization are discussed. 1.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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