Dependability Modeling and Assessment in UML-Based Software Development
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
Assessment of software nonfunctional properties (NFP) is an important problem in software development. In the context of model-driven development, an emerging approach for the analysis of different NFPs consists of the following steps: (a) to extend the software models with annotations describing the NFP of interest; (b) to transform automatically the annotated software model to the formalism chosen for NFP analysis; (c) to analyze the formal model using existing solvers; (d) to assess the software based on the results and give feedback to designers. Such a modeling→analysis→assessment approach can be applied to any software modeling language, be it general purpose or domain specific. In this paper, we focus on UML-based development and on the dependability NFP, which encompasses reliability, availability, safety, integrity, and maintainability. The paper presents the profile used to extend UML with dependability information, the model transformation to generate a DSPN formal model, and the assessment of the system properties based on the DSPN results.
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 | 0.012 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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