Are apps ready for new Android releases?
Bibliographic record
Abstract
Context: Android operating system always brings new releases and updates to improve security, increase performance and bring a better user experience. When Google announces a new release, a whole chain of changes is triggered in cascade, causing many compatibility issues. Objective: This study focus at performing a quantitative and qualitative analysis on the state of apps readiness for new Android releases over time. Method: We performed an empirical study to map apps readiness to different Android versions. We developed a Repository Mining Tool to analyse 8420 open-source repositories, detecting 2118 Android projects and when they were adapted to different Android versions along their lifetimes. Results: Our results show that Android apps have became "less ready" over time. We found that 76.45% of the analysed apps were ready for Android Lollipop 5.0 (API level 21) release, in October 2014. Though only 5.46% were ready for Android 10 (API level 29), in September 2019. In addition, our results show that when apps are adapted to an Android version, 59.41% perform the adaptation until the new Android release month, 95% are adapted twelve months after the release, and 99.16% are adapted two years later. Conclusion: Our findings reveal implications that affect not only the Android or mobile development research field and developers, they also reveal implications that points to Google's policies and Android final users as well.
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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 | 0.003 | 0.036 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".