Concolic testing for models of state-based systems
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
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 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