Modelling the Structure of Reusable Solutions for Architecture-Based Quality Evaluation
Bibliographic record
Abstract
When designing cloud applications many decisions must be made like the selection of the right set of software components. Often, there are several third-party implementations on the market from which software architects have the choice between several solutions that are functionally very similar. Even though they are comparable in functionality, the solutions differ in their quality attributes, and in their software architecture. This diversity hinders automated decision support in model-driven engineering approaches, since current state-of-the-art approaches for automated quality estimation often rely on similar architectures to compare several solutions. In this paper, we address this problem by contributing with a metamodel that unifies the architecture of several functional similar solutions, and describes the different solutions' architectural degrees of freedom. Such a model can be used later to extend the process of reuse from reusing libraries to reusing the corresponding models of these libraries with the lasting benefit of automated decision support at design-time that supports decisions when deploying applications into the cloud. Finally, we apply our approach on two intrusion detection systems.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".