Reverse Engineering iOS Mobile 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
As a result of the ubiquity and popularity of smart phones, the number of third party mobile applications is explosively growing. With the increasing demands of users for new dependable applications, novel software engineering techniques and tools geared towards the mobile platform are required to support developers in their program comprehension and analysis tasks. In this paper, we propose a reverse engineering technique that automatically (1) hooks into, dynamically runs, and analyzes a given iOS mobile application, (2) exercises its user interface to cover the interaction state space and extracts information about the runtime behaviour, and (3) generates a state model of the given application, capturing the user interface states and transitions between them. Our technique is implemented in a tool called iCrawler. To evaluate our technique, we have conducted a case study using six open-source iPhone applications. The results indicate that iCrawler is capable of automatically detecting the unique states and generating a correct model of a given mobile application.
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 | 0.000 | 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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