Signature-based detection of behavioural deviations in flight simulators - Experiments on FlightGear and JSBSim
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
Flight simulators are systems composed of numerous off-the-shelf components that allow pilots and maintenance crew to prepare for common and emergency flight procedures for a given aircraft model. A simulator must follow severe safety specifications to guarantee correct behaviour and requires an extensive series of prolonged manual tests to identify bugs or safety issues. In order to reduce the time required to test a new simulator version, this paper presents rule-based models able to automatically identify unexpected behaviour (deviations). The models represent signature trends in the behaviour of a successful simulator version that are compared to the behaviour of a new simulator version. Empirical analysis on nine types of injected faults in the popular FlightGear and JSBSim open source simulators shows that our approach does not miss any deviating behaviour considering faults which change the flight environment, and that we are able to find all the injected deviations in 4 out 7 functional faults and 75% of the deviations in 2 other faults.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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