1.6.2 Formalizing a Structured Natural Language Requirements Specification Notation
Bibliographic record
Abstract
Abstract Requirements specification notations are developed by organizations in order to meet their specific needs. For example, the Threads‐Capabilities notation, an in house notation at Raytheon Systems Canada, Ltd., has been developed and used for specifying their complex, large scale, air traffic control systems. It is a semi‐formal, structured, natural language notation. In this work, we investigate how to make this semi‐formal notation more rigorous (i.e., formal) by developing and applying a new formalization process to it. By doing this, we can obtain the advantages of formal methods (precise, unambiguous, automatic generation of test specifications, automated typechecking, etc.) while retaining the style and readability of the original notation. We call the formalized notation the Stimulus Response Requirements Specification (SRRS) notation. Our results have been successful for the specific notation. The formalized notation has been demonstrated to reduce the time and improve the quality of the requirements specifications. There is additional training time, however, needed to learn to use the notation and tools.
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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 | 0.013 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".