Exploring Strategies for Guiding Symbolic Analysis with Machine Learning Prediction
Bibliographic record
Abstract
To improve the scalability of symbolic analysis tools, one observation is that analysis resources are wasted on analyzing unsatisfiable paths, which are not possible in reality. While existing works attempt to predict the satisfiability of a program path without spending resources to analyze it, the performance of these predictor models are far from perfect. In this work, we attempt to understand how model predictions, even if imperfect, can be most effectively used to reduce the time required to analyze satisfiable paths. This work studies the sometimes complex interactions between model performance, analysis domain properties such as the distribution of path analysis costs and distribution of satisfiable paths, the design of symbolic analysis tools being used, and the algorithm used to prioritize and select paths for analysis. Using a novel simulation methodology, we study this problem and find that a number of factors can have as large an effect on symbolic analysis performance as improved predictors. Finally, we conclude with a couple of observations about how to best integrate machine learning prediction into symbolic analysis.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".