Contract by systems modelling: a case study on the FDA principles of software validation
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
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.
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 | 0.001 | 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