Eliciting contextual requirements at design time: A case study
Bibliographic record
Abstract
The need to consider context in order to understand requirements is established in requirements engineering. Recently, this has been discussed more intensively for sociotechnical systems, which offer a rich spectrum of different operating contexts. Contextual requirements proved valuable to model requirements together with the context they are valid in, but there is a lack of research on how to derive them from stakeholder needs. Our goal in this paper is to explore the usefulness of existing requirements elicitation techniques for the identification of contextual requirements early, i.e. at design time. In a case study we investigate end-user viewpoints, together with interviews, scenarios, prototyping, goal-based analysis, and groupwork as a means to elicit and clarify contextual requirements already at design time. In our case study a certain combination of the applied requirements elicitation techniques stood out as most beneficial for the identification of contextual requirements. In addition, we discovered valuable indicators of differences in the operative context, for example when end-users cannot agree on refinements of specific requirements. Designers and operators of adaptive systems might benefit by taking such conflicts and resulting contextual requirements into account.
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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 | 0.011 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".