The impact of domain knowledge on the effectiveness of requirements idea generation during requirements elicitation
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
It is believed that the effectiveness of requirements engineering activities depends at least partially on the individuals involved. One of the factors that seems to influence an individual's effectiveness in requirements engineering activities is knowledge of the problem being solved, i.e., domain knowledge. While a requirements engineer's having in-depth domain knowledge helps him or her to understand the problem easier, he or she can fall for tacit assumptions of the domain and might overlook issues that are obvious to domain experts. This paper describes a controlled experiment to test the hypothesis that adding to a requirements elicitation team for a computer-based system in a particular domain, requirements analysts that are ignorant of the domain improves the effectiveness of the requirements elicitation team. The results, although not conclusive, show some support for accepting the hypothesis. The results were analyzed also to determine the effect of creativity, industrial experience, and requirements engineering experience. The results suggest other hypotheses to be studied in the future.
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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 | 0.001 |
| 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