A Framework for Iterative, Interactive Analysis of Agent-Goal Models in Early Requirements Engineering.
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
Abstract. The early stage of domain analysis in requirements engineering is critical for understanding the stakeholders, their needs, problems, and how views of these problems differ. We advocate methods for early domain exploration which provoke iteration over captured knowledge, prompting analysts and stakeholders to review what is known, helping to guide elicitation, and facilitating early scoping and decision making. Specifically, we provide a framework to support interactive, iterative analysis over goal- and agentoriented (agent-goal) models. The framework will allow for multiple types of analysis questions, manage alternative evaluations over a model, manage interactive results, capture model assumptions and arguments, and support iteration over all constructs. Initial case study experience shows that interactive evaluation provokes model iteration and domain exploration. Further case studies will be developed to test the benefits of framework expansions. Keywords: Goal-and Agent-Oriented Models, Early RE, Model Analysis 1
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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