Bibliographic record
Abstract
The advent of modeling in software engineering, like other engineering fields, has revolutionized the formalism and pace of software development. However, software applications are not built from scratch, instead, other existing software artifacts are reused and combined with new artifacts. This notion of software reuse has been in existence for decades. When structural models such as class diagrams are reused, the reusing and reused models often need to be merged and the result visualized to the modeler. However, layout mechanisms such as GraphViz, JGraphX, and other related layout tools do not retain the original layout and rather arbitrarily layout the merged models. Therefore, important information that corresponds to the mental map of a modeler and is conveyed by the specific layout is currently lost. This paper aims to establish a robust layout algorithm called rpGraph that retains the general layout of the reusing and reused models after merging. rpGraph uses the relative positioning of model elements to inform the positioning of merged model elements. Our findings are evaluated with 20 example model reuses from a library of reusable software model artifacts. A comparison of the merged layouts of rpGraph, GraphViz, and JGraphX shows that rpGraph performs better in terms of retaining the original layouts.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.015 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.004 |
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".