IRIS-TS: DETECTING INTERACTIONS BETWEEN REQUIREMENTS IN DOORS
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract. This paper investigates the problem of requirement interactions which occurs due to negative relationships between requirements when developing software systems. This paper presents IRIS-TS (Requirements Interactions using Semi-formal methods- Tool Support) which identifies and detects requirement interactions using semi-formal methods in any software domain. IRIS-TS is implemented as an independent add-on module that can be added to DOORS (which is one of the most famous and commonly used requirements management tools). This paper presents also a case study in which the proposed IRIS-TS approach was successfully used as an add-on module in DOORS to detect interactions between smart homes requirements which represent a new application domain for interaction detection. The presented case study is the first comprehensive effort to fully detect interactions in the smart homes domain.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.003 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it