Evaluating Methodologies: A Requirements Engineering Approach Through the Use of an Exemplar
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. Systems development methodologies continue to be a central area of research in software engineering. As the nature of applications and systems usage move increasingly towards open networked environments, not only are new methodologies required, but new ways for evaluating methodologies for these new environments are also required. The agent-oriented approach to software engineering introduces concepts such as pro-activeness and autonomy to achieve more flexible and robust systems for complex applications environments. A number of AOSE methodologies have been proposed. In order to evaluate and compare these methods in depth, we proposed the use of a common exemplar – a detailed application setting within which each of the methodologies will be worked out. The evaluation method emphasizes a requirements engineering perspective. In this paper we show how to apply this exemplar to evaluate three agent-oriented methodologies.
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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 | 0.000 | 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 | 0.004 |
| 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