SHEDDING LIGHT ON CUSTOMER REQUIREMENT SPECIFICATIONS, FUNCTIONAL SPECIFICATIONS AND REQUIREMENTS LISTS – HOW ENGINEERS LEARN THE CORRECT DOCUMENTATION OF REQUIREMENTS
Bibliographic record
Abstract
Engineering students face a confusion of requirements and product properties during task clarifi-cation in product development projects. As requirements are mainly documented in the form of desired product properties, customer needs and expectations may not be sufficiently considered during the development of new and innovative products.This paper presents the results of a systematic litera-ture analysis of existing requirement documentation forms and analyses the documentation process as it is taught to engineering students. Requirements are often documented through a tripartite process of translating customer ex-pectations from the customer requirement specification to the functional specification, while the requirements list provides the base for the product development process. The contents of these documents appear theoretically different, however, they are barely distinguishable from each other in practice.Therefore, the paper provides a new model-based un-derstanding for the documentation of requirements through gradual concretisation of requirements during the product development process, leading gradually from customer needs and expectations to requirements. Engi-neering students must be able to gradually concretise requirements then document desired product properties to avoid early fixation on specific product properties. Un-dergraduate and graduate engineering students should be taught to consider requirements according to the pro-posed approach as it enables prospective engineers al-ready in the early phases of their engineering education to design highly complex technical products. Thus, the model provides a valuable base for formally supported requirements documentation and the systematic determi-nation of product properties
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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 | 0.036 | 0.085 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.002 | 0.010 |
| Scholarly communication | 0.015 | 0.031 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.004 | 0.006 |
| 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".