Graph Search of Software Models Using Multidimensional Scaling
Bibliographic record
Abstract
Software models formalize the requirements, structure and behavior of a system or application. They represent essential artifacts that simplify the process of software development. Software repositories have been developed to store models in order to facilitate the reuse of know-how from software projects; however, methods for searching these model repositories are not very efficient. Specifically, while being more scalable, general-purpose keyword search is not suitable for model search because it does not consider the structure that is inherent in software models: a good search algorithm should consider the model structure as well as the knowledge concentrated in the metamodel. On the other hand, existing approaches that consider the structure while querying software models are limited to only specific domains such as Business Process Models (BPMs). In this paper, we introduce MultiModGraph, an efficient approach for indexing and searching model repositories. MultiModGraph preserves the model structure and metamodel information by representing models as graphs. To enable efficient search, the approach employs multidimensional scaling to approximately map vertices of the model graph to points in space. We evaluate MultiModGraph both with respect to speed and quality of results using a real-word repository of web application models.
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 | 0.001 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.008 | 0.010 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".