A Comparison of Bugs Across the iOS and Android Platforms of Two Open Source Cross Platform Browser Apps
Bibliographic record
Abstract
Mobile app developers want to maximize their revenue and hence want to reach as large an audience as possible. In order to do this, they need to build apps for multiple platforms - like Google's Android and Apple's iOS, and maintain them in parallel. Past research has examined properties of the issues addressed in either Android or iOS, but not to compare the work between both. Our main motivation has been to determine if there were differences in how issues manifest themselves in iOS and Android, when we control for the projects, by considering the same apps across multiple platforms. In this paper, we compare issues across two mobile platforms - iOS and Android - for two open source browsers - Mozilla Firefox and Google Chromium. We consider three dimensions of study: frequency of issue report submission, fixing time of issues, and type of issues (using topic modeling on the issue description to generate the categories). We found that there were indeed differences; in particular, we found that there were more issues in the Android version of the apps and the gap with the iOS version is increasing. We observe that in both apps the fix time and type of issues are different for each platform. We also noted certain kinds of issues that may be more prevalent for different browser/platform combinations. This can advise project leads in identifying and allocating development resources to address key problem areas. Hence, issue reports seem more dependent on the platform than on the mobile app, making development and maintenance effort hard to estimate.
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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 | 0.001 | 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 | 0.002 | 0.002 |
| 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".