8.1.2 Requirements Risk Assessment ‐ Integrating QFD and Risk Assessment
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.
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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 | 0.019 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".