A Model-driven and Template-based Approach for Requirements Specification
Bibliographic record
Abstract
Requirements specification and verification play an important role in the certification of safety-critical software (SCS). These activities are costly and error-prone because SCS exhibit a high number of requirements and most SCS manufacturers are still using natural language to specify these requirements. On one hand, natural language can introduce ambiguity and inconsistency. On the other hand, formal languages add an overhead to the requirements specification because of their complexity. Controlled Natural Languages (CNLs) fill these gaps by offering a middle-ground solution, although not yet well adopted by the industry. In this paper, we introduce an approach that combines CNLs and model-driven engineering (MDE) for requirements specification. The approach was proposed to support an industrial partner in the certification process of a SCS. Our approach uses templates and relies on two types of models: models that specify the templates, and a model of the domain of the system at hand. Using models of the templates enables to automate some requirements analysis tasks. Using a domain model allows the auto-completion and verification of requirements specified using the templates. We implemented the approach and validated it using three case studies and more than a thousand requirements. We observed that our approach and underlying templates are applicable across domains and that the templates yield requirements with better quality in terms of necessity, ambiguity, completeness, singularity, and verifiability.
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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 | 0.008 | 0.015 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.004 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".