Evaluating the Effectiveness of a Goal-Oriented Requirements Engineering Method
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 an attempt to answer the need for methods and tools in requirements engineering (RE) which are domain specific and can address the main RE objectives (REOs), and the growing interest in the goal oriented requirements engineering (GORE) approach that overcomes the inadequacy of the traditional systems analysis approaches, we systematically evaluate the KAOS method, and the Objectiver tool, using the major REOs widely accepted as being important attributes of requirements specifications. In addition, we examine whether KAOS and Objectiver meet their own selfdefined objectives. We use two target problems as a basis for the evaluation. The result of the target problems is raw data consisting of error reports and observations that support the evaluator's judgment. The evaluation itself is qualitative, not a statistical experimental evaluation. Its result will help to answer the research questions: (i) How well do KAOS and Objectiver meet the criteria established in the discipline of RE; and (ii) How well do KAOS and Objectiver achieve their own self-defined objectives.
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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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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