On the Automated Generation of UI for Template-based Requirements Specification
Bibliographic record
Abstract
Requirements specification is a critical phase of the software development life cycle where requirements are identified and documented. To mitigate the ambiguity of natural language, templates can be adopted for the semi-formal specification of requirements. Automated specification support is important as it simplifies and expedites the specification process. However, developing the User Interface (UI) for template-based specification is demanding in terms of time and resources. In this paper, we propose a model-driven approach for generating UIs that support template-based requirements specification. We support the generation through mapping rules that link the template metamodel to the UI metamodel. We provide a systematic four-step process for the generation of UI from an input template, which includes preparation, components identification, rendering, and integration. We implemented our approach into our tool MD-RSuT for the automated generation of UI. To evaluate our approach, we compared it to manual UI development and assessed the quality of generated UIs. Our evaluation indicated that the approach provides multiple advantages over manual development, and the generated UIs adhere to UI design principles of structure, simplicity, visibility, feedback, tolerance, and reuse.
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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 | 0.004 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".