Bibliographic record
Abstract
Use cases are promising vehicles for specifying requirements. However, obtaining well-organized use case models is difficult during software evolution. The thesis proposes to address the issue by refactoring use case models. Refactoring is a program transformation approach for iterative software development. Its concept is introduced to use case models in Cascaded Refactoring. The thesis introduces major research involved in refactoring use case models. It defines a use case metamodel to formalize use cases. The three-level metamodel covers the environment or context of a use case model, the structure of use cases in terms of episodes, and the event or message passing details of a scenario. The thesis presents a process algebra semantics for the use case model. The episode semantics is provided from the literature. The semantics of a single use case is defined in terms of the episode model. The semantics of the use case model is defined in terms of the individual use cases and their relationships. The thesis identifies a list of properties that need to be preserved during refactoring. It defines fifty-three use case refactorings, which are described using a template covering the refactoring description, arguments, preconditions, postconditions and verification of behavior preservation. The thesis also introduces a tool for use case modeling and refactoring. The tool helps validate the use case metamodel and refactorings on two case studies, which demonstrate that refactoring use case models is feasible and practical. Based on these case studies, the thesis discusses the nature of use case evolution and provides some guidelines for the refactoring process.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.015 | 0.036 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.004 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.010 | 0.011 |
| Open science | 0.006 | 0.005 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 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".