Early evaluation of software performance based on the UML performance profile
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 Profile for Schedulability, Performance and Time recently adopted by OMG defines performance extensions via the Unified Modeling Language (UML) stereotypes, tagged values and constraints. In order to conduct quantitative performance analysis of a UML model with performance annotations, one must first translate it into a performance model, then solve the generated model with an existing performance analysis tool. This paper proposes a method for transforming automatically an annotated UML model into a simulation-based performance model. The UML model represents the software architecture, the deployment of software on hardware resources, and a set of key performance scenarios. The transformation was implemented in Extensible Stylesheet Language for Transformations (XSLT). It takes as input an annotated UML model in eXtensible Markup Language (XML) Metadata Interchange (XMI) format, and produces as output a CSIM18 compile-ready simulation model. The transformation is done in two steps: a) from the UML input model to an XML intermediate form, and b) from the later to the compile-ready code of the simulation model. The intermediate form was designed to be independent of the specific performance model, so other types of performance models can be generated from it.
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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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