Adapting Linux for mobile platforms: An empirical study of Android
Bibliographic record
Abstract
To deliver a high quality software system in a short release cycle time, many software organizations chose to reuse existing mature software systems. Google has adapted one of the most reused computer operating systems (i.e., Linux) into an operating system for mobile devices (i.e., Android). The Android mobile operating system has become one of the most popular adaptations of the Linux kernel with approximately 60 millions new mobile devices running Android each year. Despite many studies on Linux, none have investigated the challenges and benefits of reusing and adapting the Linux kernel to mobile platforms. In this paper, we conduct an empirical study to understand how Android adapts the Linux kernel. Using software repositories from Linux and Android, we assess the effort needed to reuse and adapt the Linux kernel into Android. Results show that (1) only 0.7% of files from the Linux kernel are modified when reused for a mobile platform; (2) only 5% of Android files are affected by the merging of changes on files from the Linux repository to the Android repository; and (3) 95% of bugs experienced by users of the Android kernel are fixed in the Linux kernel repository. These results can help development teams to better plan software adaptations.
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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 | 0.007 | 0.070 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".