Bibliographic record
Abstract
The majority of the present software systems, such as those that run on automatic banking machines (ABMs), on mobile devices, and on the Web, are interactive in nature. Therefore, it is critical to precisely understand, identify, and document the services that an interactive software system will provide from the viewpoint of its potential users. A large and important class of models that these services encapsulate is use cases (Jacobson, Christerson, Jonsson, & Övergaard, 1992). In the last few years, use cases have become indispensable as means for behavioral modeling of interactive software systems. They play a crucial role in various software development activities, including estimating development cost (Anda, 2003), eliciting behavioral requirements, and defining test cases. It is well known that addressing quality early is crucial to avoid the propagation of problems to later artifacts (Moody, 2005). With the increasing deployment of use cases as early artifacts in software process environments, the question of how these models should be developed so as to attain high quality arises. In response, this article focuses on the use case modeling process (the act of constructing use case models) and, based on the notion of patterns (Appleton, 1997), proposes a systematic approach towards the development of use case models. The rest of the article is organized as follows. The background and related work necessary for the discussion that follows is outlined. This is followed by the presentation of a pattern-oriented use case modeling process for systematically addressing the semiotic quality of use case models in a feasible manner. Next, challenges and directions for future research are outlined, and finally, concluding remarks are given.
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 | 0.011 | 0.025 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.006 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".