Debunking the Myth That Upfront Requirements Are Infeasible for Scientific Computing Software
Bibliographic record
Abstract
Many in the Scientific Computing Software community believe that upfront requirements are impossible, or at least infeasible. This paper shows requirements are feasible with the following: i) an appropriate perspective ('faking' the final documentation as if requirements were correct and complete from the start, and gathering requirements as if for a family of programs); ii) the aid of the right principles (abstraction, separation of concerns, anticipation of change, and generality); iii) employing SCS specific templates (for Software Requirements and Module Interface Specification); iv) using a design process that enables change (information hiding); and, v) the aid of modern tools (version control, issue tracking, checking, generation and automation tools). Not only are upfront requirements feasible, they provide significant benefits, including facilitating communication, early identification of errors, better design decisions and enabling replicability. The topics listed above are explained, justified and illustrated via an example of software developed by a small team of software and mechanical engineers for modelling the solidification of a metal alloy.
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.
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 | 0.029 | 0.087 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.035 |
| Scholarly communication | 0.009 | 0.031 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.007 | 0.024 |
| Insufficient payload (model declined to judge) | 0.006 | 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".