UML Consistency Rules
Bibliographic record
Abstract
UML models are standard artifacts used by software engineers for designing software. As software is designed, different UML diagram types (e.g., class diagrams and sequence diagrams) are produced by software designers. Since the various UML diagram types describe different aspects of a software system, they are not independent but strongly depend on each other, hence they must be consistent. Inconsistencies cause faults in thefi nal software systems. It is, therefore, paramount that they get detected, analyzed, andfi xed. Consistency rules are a useful tool proposed in the literature to detect inconsistencies. They categorize constraints that help in identifying inconsistencies when violated. This case study aims at collecting and analyzing UML models with OCL consistency rules proposed in the literature and at promoting the development of a reference benchmark that can be reused by the (FM-)research community. We collected 33 UML consistency rules and 206 different UML diagrams contained in 34 open-source UML models presented in the literature. We propose an FM-based encoding of the consistency rules in OCL. This encoding allows analyzing whether the consistency rules are satisfied or violated within the 34 UML models. To assess the proposed benchmark, we analyzed how the UML models, consistency rules, diagram types contained in the benchmark help in assessing the consistency of UML models, and the consistency of diagrams across the different software development phases. Our results show that the considered UML models and consistency rules allowed identifying 2731 inconsistencies and that those inconsistencies refer to different software development phases. We concluded that the considered UML models and consistency rules could be considered as an initial benchmark that can be further extended by the research community.
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 | 0.019 | 0.085 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.012 | 0.007 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.022 | 0.019 |
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".