A Methodology for the Formal Verification of Dynamic Fault Trees Using HOL Theorem Proving
Bibliographic record
Abstract
Dynamic Fault Trees (DFTs) are increasingly being used for modeling the failure behaviors of systems, particularly dynamic behaviors that cannot be captured using conventional combinatorial models. Traditionally, paper and pencil or simulation are used for the analysis of DFTs. While the former can provide generic expressions for the probability of failure, its results are prone to human errors. The latter method is based on sampling and the results are not guaranteed to be complete. Leveraging upon the expressive and sound nature of higher-order logic (HOL) theorem proving, it has been recently proposed for the analysis of DFTs algebraically. In this paper, we propose a novel methodology for the formal analysis of DFTs, based on the algebraic approach, while capturing both the qualitative and probabilistic aspects using theorem proving. In this paper, we further enrich the DFT library in HOL by providing the formalization of spare gates with a shared spare and the verification details of their probabilistic behavior. To demonstrate the utilization of our methodology, we apply it for the formal analysis of two safety-critical systems, namely, a drive-by-wire system and a cardiac assist system.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".