AGADUC: Towards a More Precise Presentation of Functional Requirement in Use Case Mod
Bibliographic record
Abstract
Use case (UC) models describe functional requirements as a set of interactions between a software system and its environment. In essence, UC descriptions state a set of workflows that would allow a system's user to benefit from its services. It is critical that designers have a common and precise understanding of what these workflows are. Otherwise they are in danger of building the 'wrong' system. Traditionally, UC descriptions are authored using natural language, which as shown in this article, proves to be a poor vehicle, and insufficient, to describe the underlying workflows. Simply, the inherit ambiguity in natural language leads to misinterpretations and misunderstandings. UC diagrams do not provide any information about the dependencies between workflows spanning several UCs. In this paper, we present the process AGADUC, which systematically generate activity-like diagrams that represent the embedded workflows in the UC textual descriptions. A GADUC provides a great deal of information regarding how UCs are dependent on each other, without the need to iterate through several pages of UC descriptions. Using activity-like diagram ensures that all stakeholders have a precise and consistent understanding of the workflows. A case study conducted on a simplified Library case is presented and have shown that AGADUC overcomes many limitations in traditional UC models. The featured tool AREUCD automates the AGADUC process and it is demonstrated within the case study
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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 | 0.014 | 0.032 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.010 | 0.013 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.008 | 0.004 |
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".