Document driven certification of computational science and engineering software
Bibliographic record
Abstract
This paper presents a documentation and development methodology to facilitate the certification of Computational Science and Engineering (CSE) software that is produced by professional end user developers to solve mathematical models of physical systems. To study the problems faced during quality assurance and certification activities, a case study was performed on legacy software used by a nuclear power generating company for safety analysis in a nuclear reactor. Although no errors were uncovered in the code, the documentation still needed significant updating for certification, since its was incomplete and inconsistent. During the case study, 27 issues were found with the documentation. This work proposes improvements to the case study software and other CSE software via a new template for the Software Requirements Specification (SRS) that clearly and sufficiently states the requirements, while satisfying the desired qualities for a good SRS. For developing the design and implementation, this paper suggests Literate Programming (LP) as an alternative to traditional structured programming. Literate Programming documents the numerical algorithms and the logic behind the development and the code together in the same document, the Literate Programmer's Manual (LPM). The LPM is developed in connection with the SRS. The explicit traceability between the theory, numerical algorithms and implementation (code), facilitates completeness and consistency, and simplifies the process of verification and the associated certification.
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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 | 0.020 | 0.071 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".