Standards in software development and modeling
Bibliographic record
Abstract
There are important standardization bodies that actually create very good and widely used standards in engineering and development.This is prominent in other engineering domains, but less common in computer science.We may speculate about the reasons, but it may be that computer science is relatively young, and therefore, techniques and methods evolve frequently, and standards may hinder this form of innovation.A second reason may be that in computer science, large companies are developing the de facto standards that are not necessarily becoming formal standards.But in computer science, standards also ensure compatibility, interoperability, reliability, security, reusability, and potentially many other good properties across services, applications, systems, and technologies.And we all know some key categories and examples of relevant standards, such as programming language standards (e.g., ISO/IEC 9899-for C, Java Community Process (JCP) specs-for Java, ECMA-262/ISO/IEC 16262-for JavaScript, HTTP/HTTPS (RFC 9110) protocol-for web communication, and RFC 8259-for JSON).The most relevant standards for Software & Systems Engineering are UML (first by the OMG and later by ISO/IEC 19505), IEEE 830 / ISO/IEC/IEEE 29148-for Software Requirements Specification, and the newly emerging standards around the digital twin technologies stack that are in discussion by the Digital Twin Consortium (DTC) and the Industrial Digital Twin Association (IDTA).As a side note, B
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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 | 0.030 | 0.037 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.009 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.009 | 0.012 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.006 | 0.009 |
| Insufficient payload (model declined to judge) | 0.006 | 0.006 |
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".