A towards an extended relational algebra for software architecture
Why this work is in the frame
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Bibliographic record
Abstract
Software architecture is often structured as box-and-arrow graphs and has important implications for system development and maintenance. We propose an extended relational algebra to support presentation and manipulation of both architectural structures and implications. The core structure of this algebra is the extended architectural relation (EAR). An EAR is a mapping from an architectural relation (AR) to a multi-set of attributes (M), where the AR is an ordinary relation representing an architectural structure, and the M represents a multi-set representing a type of architectural implication. A set of EAR operations is then defined to support EAR manipulations. The main advantage of this extended algebra over ordinary relational algebras is that the architectural implications (the M part) are presented and manipulated together with the architectural structures (the AR part). This paper first discusses why we propose the algebra, then briefly introduces what the algebra is, and finally describes how to use the algebra in a real scenario.
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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 | 0.311 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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