Stepwise Design with Message Sequence Charts
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Use cases are useful in various stages of the software process. They are very often described using text that has to be interpreted by system designers. This could lead to implementation errors. Another drawback of using such informal notations is that automating the process of moving from use cases to design specification is difficult, if not impossible. It would be beneficial to represent use cases in an unambiguous way, thereby reducing the probability of misunderstanding and allowing for automation of various activities in the software process. Message Sequence Charts (MSC) is a formal language and widely used in telecommunications for the specification of the required behaviors. In this paper, we use MSC for describing use cases and we propose an approach for stepwise refinement from high-level use cases in MSC to design MSCs that contain more details about the internal components of the system and their interactions. The refinement steps are done by the designer and guided by the system architecture. For each step, the newly obtained MSC is validated automatically against the previous MSC using a conformance relation between MSCs.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.004 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it