Contract by systems modelling: a case study on the FDA principles of software validation
Bibliographic record
Abstract
Certification has been a concern amongst the software engineering community for the past few decades and is becoming a major concern today. Several organisations, in charge of certification, have published guidance documents to describe this crucial activity. Indeed, these organisations, through their documents, aim to establish a common understanding between software producers and certifiers (evaluators). These guidance documents use natural language in specifying recommendations, because of the wide audience to which they are addressed and the consequent need for simplicity. However, the specification is not sufficiently explicit and precise to be able to impose a contract (obligation) between the two parties. In this paper, we illustrate this problem as it appears in the guidance documents published by the US Food and Drug Administration (FDA) to validate medical device software. By bearing in mind the clear distinction between products and processes, we use the Product/process (P/p) method to model the Quality Planning activity of the FDA validation approach. By using P/p modelling, we present a simplified representation for the FDA validation activities. In essence, the P/p methodology takes a general systems approach. It is appropriate to a variety of areas and has proven its applicability in many fields.
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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 | 0.014 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.007 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".