Graph Search of Software Models Using Multidimensional Scaling
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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 it