Assurance cases for scientific computing software
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Assurance cases, which provide an organized and explicit argument for correctness, should be used for certifying Scientific Computing Software (SCS), especially when the software impacts health and safety. Assurance cases have already been effectively used for safety cases for real time systems. Their advantages for SCS include engaging domain experts, producing only necessary documentation, and providing evidence that can potentially be verified/replicated by a third party. This paper illustrates assurance cases for SCS through the correctness case for 3dfim+, an existing medical imaging application. No errors were found in 3dfim+. However, the example still justifies the value of assurance cases, since the existing documentation is shown to have ambiguities and omissions, such as an incompletely defined ranking function and missing details on the coordinate system convention adopted. In addition, a potential concern for the software itself is identified: running the software does not produce any warning about the necessity of using data that matches the assumed parametric statistical model.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it