Goal-Driven Exploration for Android Applications
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
This paper proposes a solution for automated goal-driven exploration of Android applications - a scenario in which a user, e.g., a security auditor, needs to dynamically trigger the functionality of interest in an application, e.g., to check whether user-sensitive info is only sent to recognized third-party servers. As the auditor might need to check hundreds or even thousands of apps, manually exploring each app to trigger the desired behavior is too time-consuming to be feasible. Existing automated application exploration and testing techniques are of limited help in this scenario as well, as their goal is mostly to identify faults by systematically exploring different app paths, rather than swiftly navigating to the target functionality. The goal-driven application exploration approach proposed in this paper, called GoalExplorer, automatically generates an executable test script that directly triggers the functionality of interest. The core idea behind GoalExplorer is to first statically model the application's UI screens and transitions between these screens, producing a Screen Transition Graph (STG). Then, GoalExplorer uses the STG to guide the dynamic exploration of the application to the particular target of interest: an Android activity, API call, or a program statement. The results of our empirical evaluation on 93 benchmark applications and the 95 most popular GooglePlay applications show that the STG is substantially more accurate than other Android UI models and that GoalExplorer is able to trigger a target functionality much faster than existing application exploration techniques.
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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 | 0.000 |
| 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 it