A Vision Towards A Conceptual Basis for the Systematic Treatment of Uncertainty in Goal Modelling
Bibliographic record
Abstract
Goal modelling is one the most important early activities in requirements engineering. Here, we describe a vision for a conceptual basis for the systematic identification and treatment of uncertainty in goal modelling. We aim to characterize the wide variety of uncertainty in goal modelling and to provide a theoretical framework for systematic uncertainty analysis. We thus adopt Walker's taxonomy which distinguishes among three dimensions of uncertainty: location, level, and nature. In addition, we propose to adapt Walker's uncertainty matrix as a heuristic tool to categorize various dimensions of uncertainty in goal modelling to serve as a conceptual framework for improving comprehension and communication of uncertainty between modellers and stakeholders and among modellers themselves. Understanding the various dimensions of uncertainty is a vital step towards the sufficient recognition and treatment of uncertainty in goal modelling activities. This in turn will help identify and prioritize critical uncertainties, which affect the goal modelling process in its entirety. We thus propose a long-term research agenda and urge community contributions in this research direction.
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.
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 | 0.057 | 0.050 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.010 | 0.005 |
| Science and technology studies | 0.005 | 0.031 |
| Scholarly communication | 0.016 | 0.032 |
| Open science | 0.006 | 0.009 |
| Research integrity | 0.008 | 0.013 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".