8.1.2 Requirements Risk Assessment ‐ Integrating QFD and Risk Assessment
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
Abstract A modified Quality Function Deployment (QFD) analysis [Hauser and Clausing, 1988] was applied to evaluate development risk for user requirements across multiple system architectures. Applying QFD to the risk assessments required tailoring the approach to capture and quantify subjective risk judgments and to reconcile them with raw risk assessment data from specific designs. Initial evaluation of development risk from each architecture showed that overall risk reduction activities were acceptable as planned. Risk assessments were also conducted on updated designs using the QFD assessment to confirm validity of the final risk reduction plans. This method extends previous work by Clausing and Cohen [1999], which described the use of QFD to capture requirements relationships, and U.S. Air Force guidance [AFMC, 1977], which describes methods for risk evaluation. It allows capture of the strength of relationship between user requirements and development risk early in a program by use of conceptual system architectures. This permits early evaluation of the trade offs between user requirements and risk mitigation efforts prior to committing a program to a functional baseline is established for user requirements and the design solution.
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 | 0.002 | 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 | 0.001 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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