Bibliographic record
Abstract
In RE models such as goal-oriented models, a complex sys-tem is directly described in terms of its purposes, which makes its functionality much easier to understand and to reason as compared to code-level implementations. Part of the difficulty in maintaining a stronger correspondence between requirements and code is possibly due to the suffi-cient modularization capabilities of traditional architectures where many functionalities do not exist in distinct modular entities. This paper reports on an investigation of how and where some distinct design requirements lead to crosscut-ting concerns when decomposed into code in goal models such as KAOS. We begin by matching our past experience in aspect discovery at the code level with a detailed require-ments modeling of the same architecture in KAOS. The dis-covered patterns are validated in an independent project where the requirements modeling and the aspect identifi-cation are separately conducted. We observe that satisfy-ing OR-decomposed subgoals in the KAOS model typically leads to tangled implementations, and agents responsible for multiple OR-refined goals should be implemented in the aspect-oriented manner.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.005 | 0.009 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".