Modelling an Aircraft Landing System in Event-B (Full Report)
Bibliographic record
Abstract
The failure of hardware or software in a critical system can lead to loss of lives. The design errors can be main source of the failures that can be introduced during system development process. Formal techniques are an alternative approach to verify the correctness of critical systems, overcoming limitations of the traditional validation techniques such as simulation and testing. The increasing complexity and failure rate brings new challenges in the area of verification and validation of avionic systems. Since the reliability of the software cannot be quantified, the \textit{correct by construction} approach can implement a reliable system. Refinement plays a major role to build a large system incrementally from an abstract specification to a concrete system. This paper contributes as a stepwise formal development of the landing system of an aircraft. The formal models include the complex behaviour, temporal behaviour and sequence of operations of the landing gear system. The models are formalized in Event-B modelling language, which supports stepwise refinement. This case study is considered as a benchmark for techniques and tools dedicated to the verification of behavioural properties of systems. The report is the full version of a paper published for the ABZ 2014 Case Study. is
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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".