Detecting requirements interactions: a three-level framework
Bibliographic record
Abstract
This paper deals with the problem of requirements interaction. We introduce a three level framework to detect requirements interactions at different levels of cost, time, and complexity. Level 2 where we use semiformal methods to detect interactions contains the main contribution of the research. Also we combine existing approaches (e.g. informal and formal) with our semiformal approach to provide a comprehensive framework for developers to use. The approach is illustrated using two case studies, one from the telecommunications domain and the other one being a lift control system. The results obtained are very encouraging with regards to the time and effort spent on requirements interaction detection.
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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 | 0.022 | 0.036 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.002 | 0.007 |
| Bibliometrics | 0.008 | 0.003 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.013 | 0.009 |
| Open science | 0.005 | 0.008 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".