Flight Incident Analysis Through Symbolic Argumentation
Bibliographic record
Abstract
At the core of every modern airliner is a software-reliant fly-by-wire system that translates pilot inputs into electronic signals to control aircraft movements. Given the safety-critical nature of these systems they include architectural constructs and mechanisms to tolerate failures related to hardware (e.g., processor or sensor failures) and software (e.g., potential bug in the code). The goal is to reach the required levels of availability and integrity validated through a certification process that includes specific verification methods to discharge specific claims. Unfortunately, the different verification procedures and associated architectural constructs are typically developed independently and make independent assumptions that can contradict each other, thereby preventing the desired behavior or invalidating the assumptions and results of a given verification procedure. To help address these problems this paper presents how a new symbolic argumentation approach can be used to analyze a real flight incident (the flight CI202 incident in 2020) by automating the verification procedures and their assumptions. Our approach describes verification plans that start at the level of certification connected to automated verification analysis on architectural models. These plans are decomposed into analysis contracts that specify what claims they verify (e.g., availability of a fly-by-wire function> 99.99%), what analysis is used to verify the model (e.g., probabilistic Fault-Tree Analysis) and what assumptions it relies on (e.g., a function is replicated over processors that fail independently of each other). These plans are integrated into a symbolic argumentation implemented as a constraint satisfaction problem that is solved with a Satisfiability Modulo Theory (SMT) solver. The CI202 flight incident analysis is presented using an argumentation hierarchy on architectural models and the analysis of potential design issues that could explain a triple computer failure. We demonstrate how our approach can reason about early design decisions by pointing to unfulfilled assumptions, contradictions, and potential workarounds that have the potential to prevent these types of incidents.
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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 | 0.006 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".