Wins and losses of algebraic transformations of software architectures
Bibliographic record
Abstract
In order to understand, analyze and modify software, we commonly examine and manipulate its architecture. For example, we may want to examine the architecture at different levels of abstraction. We can view such manipulations as architectural transformations, and more specifically, as graph transformations. We evaluate relational algebra as a way of specifying and automating the architectural transformations. Specifically, we examine Grok, a relational calculator that is part of the PBS toolkit. We show that relational algebra is practical in that we are able to specify many of the transformations commonly occurring during software maintenance and, using a tool like Grok, we are able to manipulate, quite efficiently, large software graphs; this is a "win". However, this approach is not well suited to express some types of transforms involving patterns of edges and nodes; this is a "loss". By means of a set of examples, the paper makes clear when the approach wins and when it loses.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".