Evaluating the Effectiveness of a Goal-Oriented Requirements Engineering Method
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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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 | 0.074 | 0.170 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".