Assessing the Usefulness of Assurance Cases: an Experience with the CERN Large Hadron Collider
Bibliographic record
Abstract
Assurance cases are structured arguments designed to show that a system functions properly in its operational environment. They are mandated by safety standards and are largely used in the industrial domain; however, they are typically proprietary and not publicly available. Therefore, the benefits of assurance case development are usually not rigorously documented, measured, or assessed. In this paper, we present an assurance case for the CERN Large Hadron Collider (LHC) Machine Protection System (MPS). We relied on open-source documentation for its creation and used eliminative argumentation, a well-known methodology for assurance case development. The development involved four authors with considerable experience in assurance case development, three of whom work for Critical System Labs, a small enterprise specializing in assurance case development. The process required approximately three months and led to an assurance case with 506 nodes. The results have been validated with CERN experts. Our experience shows that (a) the effort (cost and time) required to develop our assurance case is negligible compared to the time needed to develop the system, (b) eliminative argumentation helped identify 10 defeaters not detailed in the documentation we used for creation of the assurance case (and in general can identify correct defeaters with high precision and recall). In the paper, describe our experience and also discuss how the LHC assurance case helped accurately identify Key Performance Indicators for the Machine Protection System.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.067 | 0.154 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.003 | 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".