Accelerating Symbolic Analysis for Android Apps
Bibliographic record
Abstract
While tools based on symbolic execution are commonly used to analyze mobile applications, these tools can suffer from path explosion when real-world applications have more paths than available computing resources can handle. However, many of the paths are unsatisfiable, that is, no input exists that can satisfy all the path constraints and cause the path to execute. Unfortunately, analysis tools cannot determine this without constraint collection and constraint solving, which are expensive to perform. As a result, analysis tools waste valuable computational resources on unsatisfiable paths. In this work, we demonstrate that machine learning classifiers can predict unsatisfiable paths, resulting in a savings of computational resources. Our classifiers take path-level statistical features as input, and model inference can run immediately after a path is found. This saves analysis time spent on both constraint collection and constraint solving for unsatisfiable paths. We enhance the TIRO Android application analysis tool to avoid paths that are predicted to be unsatisfiable and show that a Random Forest model can achieve 95 % balanced predication accuracy in Android applications. We also show that modified TIRO is able to avoid analyzing 51 % of paths as they are unsatisfiable, resulting in a savings of 14 % of the analysis time.
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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 | 0.000 |
| 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".