Detecting inconsistencies in multi-platform mobile apps
Bibliographic record
Abstract
Due to the increasing popularity and diversity of mobile devices, developers write the same mobile app for different platforms. Since each platform requires its own unique environment in terms of programming languages and tools, the teams building these multi-platform mobile apps are usually separate. This in turn can result in inconsistencies in the apps developed. In this paper, we propose an automated technique for detecting inconsistencies in the same native app implemented for iOS and Android platforms. Our technique (1) automatically instruments and traces the app on each platform for given execution scenarios, (2) infers abstract models from each platform execution trace, (3) compares the models using a set of code-based and GUI-based criteria to expose any discrepancies, and finally (4) generates a visualization of the models, highlighting any detected inconsistencies. We have implemented our approach in a tool called CheckCAMP. CheckCAMP can help mobile developers in testing their apps across multiple platforms. An evaluation of our approach with a set of 14 industrial and open-source multi-platform native mobile app-pairs indicates that CheckCAMP can correctly extract and abstract the models of mobile apps from multiple platforms, infer likely mappings between the generated models based on different comparison criteria, and detect inconsistencies at multiple levels of granularity.
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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 | 0.005 | 0.042 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".