Experience of Building an Architecture-Based Generator Using GenVoca for Distributed Systems.
Bibliographic record
Abstract
Selecting the architecture that meets the requirements, both functional and non-functional, is a challenging task, especially at the early stage when more uncertainties exist. Architectural prototyping is a useful approach in supporting the evaluation of alternative architectures and balancing different architectural qualities. Generative programming has gained increasing attention, but it mostly deals with lower-level artifacts; hence, it usually supports lower degrees of software automation. This paper proposes an architecture-centric generative approach in facilitating architectural prototyping and evaluation. We also present our empirical experience in raising the level of abstraction to the architecture layer for distributed and concurrent systems using GenVoca. GenVoca is a generative programming approach that is used here to support the generation or instantiation of a particular architectural pattern in distributed computing based on user's selection. As a result, it can support rapid architectural prototyping and evaluation of both functional and non-functional requirements and encourage greater degrees of software automation and reuse. Lessons learned from the empirical study are also reported and could be applied to other areas.
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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 | 0.006 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".