1st International Workshop on UML Consistency Rules (WUCOR 2015)
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
The Unified Modeling Language (UML), with its 14 different diagram types, is the de-facto standard modeling language for object-oriented software modeling and documentation. Since the various UML diagrams describe different views of one, and only one, software system under development, they strongly depend on each other in many ways. In other words, the UML diagrams describing a software system must be consistent. Inconsistencies among these diagrams may be a source of faults during software development and analysis. It is therefore paramount that these inconsistencies be detected, analyzed and -- hopefully -- fixed. The goal of this workshop was to gather input and feedbacks on UML consistency rules from the community. This workshop provided an opportunity for researchers who have been working in the area of UML consistency to interact with each other at a highly interactive venue, improve the body of knowledge on UML consistency rules and discuss ideas for further research in this area. This report summarizes details of the workshop and the results obtained that day.
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 | 0.000 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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