A Method and Tool for Test Optimization for Automotive Controllers
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
Completely automatic generation of tests from formal executable test models of industrial size still looks like a “holy grail”, in spite of significant progress in model-based testing research and tool development. Realizing this, we follow a more down-to-earth approach by assuming that, even if a test model is available, the test expert manually derives powerful test fragments and what remains to be automated is chaining them into an optimal test. Focusing on this task, we develop a test optimization framework using an FSM extended with input variables and clocks, which reflects important features of Simulink/Stateflow statecharts. The test optimization is expressed as the Asymmetric Travelling Salesman Problem (ATSP). We show how this approach can be used for solving some testing problems specific to automotive controllers. We describe a proof-of-concept prototype, implementing the proposed approach, which we tested on a case study of a particular controller available along with some tests. Experiments with the prototype indicate that the approach scales well for hundreds of tests.
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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 | 0.000 | 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 | 0.000 | 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