UTL: A Unified Language for Requirements Templates
Bibliographic record
Abstract
Requirements specification is an important phase of the software development life cycle, especially for safety critical systems (SCS) due to their high number of requirements and certification constraints. The use of templates to specify requirements has been proposed in literature as they strike a balance between the ambiguity of natural language and the difficulty of using formal languages. However, existing template-based approaches use different notations, rarely provide tool support, and generally target specific types of requirements. Thus, it is often necessary to create new custom templates, but it is difficult to do so given that there is no well-defined process to follow and no unified notation to reuse. To fill this gap, we propose the Unified Templates Language (UTL), a unified language for the definition of requirements templates and a process for using the language. We leverage model-driven engineering (MDE) to build UTL. Using MDE supports the creation and evolution of templates, and it eases the extension, maintainability, and implementation of UTL. UTL was proposed to support an industrial partner in the certification of a SCS, and implemented within a requirements specification tool. In this paper, we introduce the abstract syntax, concrete syntax, well-formedness rules and the semantics of UTL. We also provide a systematic process for creating templates using UTL. We evaluate the ability of UTL to specify different types of templates, and its usability and usefulness through a user study. The results show that UTL covers different kinds of templates, and, together with its supporting tool, it eases the creation of templates.
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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 | 0.016 | 0.029 |
| Meta-epidemiology (narrow) | 0.003 | 0.004 |
| Meta-epidemiology (broad) | 0.002 | 0.005 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.010 | 0.011 |
| Open science | 0.006 | 0.006 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.014 | 0.012 |
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".