New Frontiers for Requirements Engineering
Bibliographic record
Abstract
Requirements Engineering (RE) has grown from its humble beginnings to embrace a wide variety of techniques, drawn from many disciplines, and the diversity of tasks currently performed under the label of RE has grown beyond that encom-passed by software development. We briefly review how RE has evolved and observe that RE is now a collection of best practices for pragmatic, outcome-focused critical thinking - applicable to any domain. We discuss an alternative perspective on, and de-scription of, the discipline of RE and advocate for the evolution of RE toward a discipline that supports the application of RE prac-tice to any domain. We call upon RE practitioners to proactively engage in alternative domains and call upon researchers that adopt practices from other domains to actively engage with their inspiring domains. For both, we ask that they report upon their experience so that we can continue to expand RE frontiers.
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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 | 0.045 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.003 | 0.023 |
| Scholarly communication | 0.012 | 0.041 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.009 | 0.016 |
| Insufficient payload (model declined to judge) | 0.018 | 0.004 |
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".