Automated generation of pervasive systems architectures: a detailed empirical evaluation
Bibliographic record
Abstract
The importance of having mature software development methodologies and tools for the increasingly popular pervasive systems cannot be understated. Focusing on system architectures, we previously conducted a thorough review of over 50 state of the art architectures related to pervasive systems. From the review, we elicited a set of major features that should be supported in pervasive systems, along with best practice architectures for designing such features. We then detailed a methodology, through which designers of new pervasive systems can select a set of desired features and generate a baseline architecture for their system. In this article, we evaluate our methodology with an empirical study that compares generated architectures with ones designed by subject matter experts with sufficient experience in the domain. We used different evaluation suites and measurement techniques in our comparisons. Results show that our automatically generated architectures are very comparable with, and in many cases of higher quality than, the architectures designed by subject matter experts.
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How this classification was reachedexpand
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".