Bibliographic record
Abstract
Testing models of modern cyber-physical systems is not straightforward due to timing constraints, numerous if not infinite possible behaviors, and complex communications between components. Software testing tools and approaches that can generate test cases to test these systems are therefore important. Many of the existing automatic approaches support testing at the implementation level only. The existing model-level testing tools either treat the model as a black box (e.g., random testing approaches) or have limitations when it comes to generating complex test sequences (e.g., symbolic execution). This paper presents a novel approach and tool support for automatic unit testing of models of real-time embedded systems by conducting concolic testing, a hybrid testing technique based on concrete and symbolic execution. Our technique conducts automatic concolic testing in two phases. In the first phase, model is isolated from its environment, is transformed to a testable model and is integrated with a test harness. In the second phase, the harness tests the model concolically and reports the test execution results. We describe an implementation of our approach in the context of Papyrus-RT, an open source Model Driven Engineering (MDE) tool based on the modeling language UML-RT, and report the results of applying our concolic testing approach to a set of standard benchmark models to validate our approach.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".