TimelyRep: Timing deterministic replay for Android web applications
Bibliographic record
Abstract
Summary With the constantly growing and changing requirements of app users, web techniques are used in mobile application development for better cross‐platform compatibility and online update. As the embedded web contents gain complexity, debugging web apps become a critical demand. Web replay tools can record program inputs and reproduce the same execution for debugging and performance tuning. However, traditional replay approaches are largely intended for apps with desktop interaction methods (keyboard, mouse) and require modification to the browser, which limits their applicability in mobile platforms. In this paper, we develop TimelyRep, which provides deterministic record‐and‐replay as a software library, running on commodity Android. TimelyRep can be used for app development with unmodified Android devices and for production to collect faulty execution from users. Also, we propose an efficient replay timing control mechanism and achieve higher timing precision as facing higher event rate on touchscreen devices. TimelyRep also supports cross‐device replay and can replay logged event traces on different devices, which is useful for developers to reproduce user inputs on their own devices. We evaluate TimelyRep with real‐world web applications. The results show that TimelyRep is useful for recreating program bugs and maintaining low delays for touch‐intensive web games.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".