From Natural Language to Semi-Formal Notation Requirements for Automotive Safety
Bibliographic record
Abstract
The standard ISO 26262 stipulates a “top-down” approach based on the process “V” model, by conducting a hazard analysis and risk assessment to determine the safety goals, and subsequently derives the safety requirements down to the appropriate element level. The specification of safety goals is targeted towards identified hazardous events, whereas the classification of safety requirements does not always turn out non-ambiguous. While requirement formalization turns out to be advantageous, the translation from natural language to semi-formal requirements, especially in context of ISO 26262, poses a problem. In this publication, a new approach for the formalization of safety requirements is introduced, targeting the demands of safety standard ISO 26262. Its part 8, clause 6 (“Specification and management of safety requirements”) has no dedicated work product to accomplish this challenging task. The five levels of requirements for writing safety requirements are distributed throughout the standard, increasing the probability of misapplication. For these reasons, a dedicated requirement template is proposed. It is applicable for writing new or checking existing requirements, independent of any tool. By reviewing a number of industrial relevant use cases the applicability of the new template is verified and its effectiveness is demonstrated. Furthermore, a semi-formal notation technique is shown to express these formalized requirements, including their associated attributes and resulting relationships. By following the proposed approach, we meet the obligations of ISO 26262 to write e.g. unambiguous, consistent, verifiable, and complete requirements. In the end, this has the potential to dramatically reduce the probability of systematic failures during development of automotive embedded systems.
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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 | 0.008 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".