Loud and Interactive Paper Prototyping in Requirements Elicitation: What is it Good for?
Bibliographic record
Abstract
Requirements Engineering is a multidisciplinary and a human-centered process, therefore, the artifacts produced from RE are always error-prone. The most significant of these errors are missing or misunderstanding requirements. Information loss in RE could result in omitted logic in the software, which will be onerous to correct at the later stages of development. In this paper, we demonstrate and investigate how interactive and Loud Paper Prototyping (LPP) can be integrated to collect stakeholders’ needs and expectations than interactive prototyping or face-to-face meetings alone. To this end, we conducted a case study of (1) 31 mobile application (App) development teams who applied either of interactive or loud prototyping and (2) 19 mobile App development teams who applied only the face-to-face meetings. From this study, we found that while using Silent Paper Prototyping (SPP) rather than No Paper Prototyping (NPP) is a more efficient technique to capture Non-Functional Requirements (NFRs), User Interface (UI) requirements, and existing requirements, LPP is more applicable to manage NFRs, UI requirements, as well as adding new requirements and removing/modifying the existing requirements. We also found that among LPP and SPP, LPP is more efficient to capture and influence Functional Requirements (FRs).
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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 | 0.145 | 0.304 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.003 | 0.009 |
| Scholarly communication | 0.013 | 0.017 |
| Open science | 0.005 | 0.009 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".