A SYSTEMATIC APPROACH FOR THE SPECIFICATION OF CUSTOMER REQUIREMENTS
Bibliographic record
Abstract
Specification of customer requirements is critical for designers to solve a design problem and to deliver a quality design. This paper proposes a systematic approach to formalize various customer requirements using the axiomatic theory of design modeling. The input of this formalization process is the design requirements described in natural language by the customers. The output is a structure underlying these design requirements for designers to use throughout the rest of the design process. This structure of design problem consists of three parts: product, environment, and structural as well as performance requirements. The formalization process includes two major steps: linguistic analysis and structure analysis. The linguistic analysis identifies objects and their relationships included in the customer description of a design problem through the lexical and syntactical properties of words and sentences that form the design problem. The structure analysis integrates all objects and relationships identified from linguistic analysis to form a structure of the design problem. A software prototype is developed to show the feasibility of this approach.
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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 | 0.032 | 0.041 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.001 | 0.004 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".