Raising the Bar: Assurance Cases for Scientific Software
Bibliographic record
Abstract
Assurance cases provide an organized and explicit argument for correctness. They can dramatically improve the quality of scientific software. Assurance cases are already effectively used for real-time safety critical systems. Their advantages for scientific software include engaging domain experts, producing only necessary documentation, and providing evidence that can be verified/replicated. This article illustrates assurance cases through the correctness case for 3dfim+, software for analyzing activity in the brain. The example justifies the value of assurance cases for scientific software, 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. We identified a serious concern for 3dfim+: running the software does not produce any warning about the necessity of using data that matches the parametric statistical model employed for the correlation calculations. Raising the bar for scientific software is both feasible and necessary.
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 | 0.108 | 0.359 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.009 | 0.026 |
| Scholarly communication | 0.016 | 0.034 |
| Open science | 0.006 | 0.013 |
| Research integrity | 0.018 | 0.018 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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".