Facilitating the transition from use case models to analysis models
Bibliographic record
Abstract
Use case modeling, including use case diagrams and use case specifications (UCSs), is commonly applied to structure and document requirements. UCSs are usually structured but unrestricted textual documents complying with a certain use case template. However, because Use Case Models (UCMods) remain essentially textual, ambiguity is inevitably introduced. In this article, we propose a use case modeling approach, called Restricted Use Case Modeling (RUCM), which is composed of a set of well-defined restriction rules and a modified use case template. The goal is two-fold: (1) restrict the way users can document UCSs in order to reduce ambiguity and (2) facilitate the manual derivation of initial analysis models which, when using the Unified Modeling Language (UML), are typically composed of class diagrams, sequence diagrams, and possibly other types of diagrams. Though the proposed restriction rules and template are based on a clear rationale, two main questions need to be investigated. First, do users find them too restrictive or impractical in certain situations? In other words, can users express the same requirements with RUCM as with unrestricted use cases? Second, do the rules and template have a positive, significant impact on the quality of the constructed analysis models? To investigate these questions, we performed and report on two controlled experiments, which evaluate the restriction rules and use case template in terms of (1) whether they are easy to apply while developing UCMods and facilitate the understanding of UCSs, and (2) whether they help users manually derive higher quality analysis models than what can be generated when they are not used, in terms of correctness, completeness, and redundancy. This article reports on the first controlled experiments that evaluate the applicability of restriction rules on use case modeling and their impact on the quality of analysis models. The measures we have defined to characterize restriction rules and the quality of analysis class and sequence diagrams can be reused to perform similar experiments in the future, either with RUCM or other approaches. Results show that the restriction rules are overall easy to apply and that RUCM results into significant improvements over traditional approaches (i.e., with standard templates, without restrictions) in terms of class correctness and class diagram completeness, message correctness and sequence diagram completeness, and understandability of UCSs.
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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 | 0.037 | 0.146 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.010 | 0.014 |
| Open science | 0.005 | 0.012 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.006 | 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".