Assessing the Usefulness of Assurance Cases: Experience With the Large Hadron Collider
Bibliographic record
Abstract
ABSTRACT Assurance cases (ACs) are structured arguments designed to show that a system is sufficiently reliable to function properly in its operational environment. They are mandated by safety standards and are largely used in industry to support risk management for systems; however, ACs often contain proprietary information and are not publicly available. Therefore, the benefits of AC development are usually not rigorously documented, measured, or assessed. In this paper, we empirically evaluate the effectiveness of using ACs to show that a system is reliable using a case study over the CERN Large Hadron Collider (LHC) Machine Protection System (MPS). We used open‐source documentation to create an AC over the MPS and used the Eliminative Argumentation (EA) methodology for its development. The development involved four authors with considerable experience in AC development, three of whom work for Critical System Labs, a small enterprise specializing in ACs. Our findings show that (a) the cost and time required to develop our AC is negligible compared to the effort needed to develop the system, and (b) EA helped identify defeaters (i.e., doubts in the system's reliability) that were not detailed in the documentation used for creation of the AC.
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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 | 0.113 | 0.276 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.005 | 0.007 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".