Asking the right questions to elicit product requirements
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
Eliciting precise and comprehensive product requirements from customers is of critical importance for the success of product development. In this paper, a generic process is proposed for eliciting product requirements by asking questions based on linguistic analysis. The linguistic analysis transforms a text into a graphic language called recursive object model (ROM). Two types of questions are asked in the process. One type of question, generated according to the topological structure of the ROM diagram, is domain-independent whereas the other relies on the domain of product development. A generic template is developed for generating the questions and for determining the sequence in which those questions are asked. The answers to the questions can be sought on the internet, in text books, the dictionary, the designer's own knowledge and experience, the customers and other partners involved in the product development, and/or nature itself. The generation of new questions may be based on the answers that are obtained. A software prototype is developed to support the proposed process. A case study of a rivet-setting tool design is used to illustrate the process of generating questions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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 | 0.001 | 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