Graph Generalization for Software Engineering
Bibliographic record
Abstract
Graph generalization is a powerful concept with a wide range of potential applications, while established algorithms exist for generalizing simple graphs, practical approaches for more complex graphs remain elusive. We introduce a novel formal model and algorithm (GGA) that generalizes labeled directed graphs without assuming label identity. We evaluate GGA by focusing on its information preservation relative to its input graphs, its scalability in execution, and its utility for three applications: abstract syntax trees, class graphs, and call graphs. Our findings reveal the superiority of GGA over alternative tools. GGA outperforms ASGard by an average of 5–18% on metrics related to information preservation; GGA matches 100% with diffsitter, indicating the correctness of the output. For class graphs, GGA achieves 77.1% in precision at 5, while for call graphs, it exhibits 60% in precision at 5 for a specific application problem. We also test performance for the first two applications: GGA’s execution time scales linearly with respect to the product of vertex count and edge count. Our research demonstrates the ability of GGA to preserve information in diverse applications while performing efficiently, signaling its potential to advance the field.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.006 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".