Bibliographic record
Abstract
This thesis addresses the problem of verifying the application of a Pattern Language in a design that is built based upon the patterns of the language in a Model-Driven approach. Exploiting the ideas of compilers, we propose a process named Pattern Language Verifier (PLV). We argue that building a PLV for a given Pattern Language, requires the Structural, Syntactic, and Semantic rules of the language to be precisely defined. We present three formalisms for defining these three groups of rules. PLV is a profile-driven process and assumes that a UML Profile is already defined for the underlying Pattern Language. PLV consists of four phases: Pattern Structural Verifier (PSV), Pattern Language Syntactic Verifier (PTV), Pattern Language Semantic Verifier (PMV), and Pattern Language Advisor (PLA). PSV verifies the structure of every single pattern used in the design model. PTV verifies the relationships between the detected patterns. PMV verifies the semantic aspects of the patterns. PLA reports the problems to the designer and guides him/her in fixing the errors. For the case study, a group of enterprise architectural patterns is selected as the Pattern Language. The Structural, Syntactic, and Semantic rules of the language are defined using the proposed formalism, and a UML Profile is defined for the language. A PLV is designed and implemented as an integration into an open source modeling tool. The tool is then utilized in designing a sample web application: Online Student Registration System. The usefulness of the tool is represented by walkthrough scenarios that show finding the mistakes in the model and helping the designer repair the detected problems
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 | 0.057 | 0.160 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.009 |
| Scholarly communication | 0.006 | 0.016 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".