Debunking the Myth that Upfront Requirements are Infeasible for\n Scientific Computing Software
Bibliographic record
Abstract
Many in the Scientific Computing Software community believe that upfront\nrequirements are impossible, or at least infeasible. This paper shows\nrequirements are feasible with the following: i) an appropriate perspective\n("faking" the final documentation as if requirements were correct and complete\nfrom the start, and gathering requirements as if for a family of programs); ii)\nthe aid of the right principles (abstraction, separation of concerns,\nanticipation of change, and generality); iii) employing SCS specific templates\n(for Software Requirements and Module Interface Specification); iv) using a\ndesign process that enables change (information hiding); and, v) the aid of\nmodern tools (version control, issue tracking, checking, generation and\nautomation tools). Not only are upfront requirements feasible, they provide\nsignificant benefits, including facilitating communication, early\nidentification of errors, better design decisions and enabling replicability.\nThe topics listed above are explained, justified and illustrated via an example\nof software developed by a small team of software and mechanical engineers for\nmodelling the solidification of a metal alloy.\n
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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 | 0.025 | 0.083 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.023 |
| Scholarly communication | 0.009 | 0.025 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.005 | 0.015 |
| Insufficient payload (model declined to judge) | 0.007 | 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".