Visualizing Aspect-Oriented Goal Models with AoGRL
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
As goal models can be large and complex even for small problems, it is often a challenge to aptly visualize them and to efficiently structure them for maintenance and reuse activities. The Goal-oriented Requirement Language (GRL) based on i* and the Non- Functional Requirements (NFR) Framework is no exception regarding these challenges. We argue that new ways of visualizing GRL goal models are needed and propose to use Aspect-oriented GRL (AoGRL) to improve the current structure of GRL models and their visualization. The paper presents a case study to evaluate the modularity, understandability, reusability, maintainability, and scalability of AoGRL models compared to GRL models. The evaluation is carried out based on metrics adapted from literature. The evaluation suggests that AoGRL models are more scalable than GRL models and exhibit better modularity, understandability, reusability, and maintainability requirements engineering approaches such as use cases [11], viewpoints [18] and goals [1]. However work on goals and aspects still needs more investigation.
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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 | 0.001 | 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 | 0.001 |
| Open science | 0.000 | 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