ODACE-RMS: A Remote Web-Based Platform for Automated Multi-Device Android Testing and Certification
Bibliographic record
Abstract
The evolving nature of the software industry has increased the complexity and cost of software testing. This paper highlights the critical need for automation in software testing, specifically for mobile Android device certification. We introduce ODACE-RMS, a platform designed to streamline the certification process by enabling the automated execution of comprehensive telecommunication test scenarios. ODACE-RMS runs as an application on a tester’s PC, featuring a browser-based interface powered by Appium. The paper also outlines ODACE-RMS’s modular architecture that combines Appium, ADB, and USB-over-IP to support remote and parallel testing. With a Spring Boot backend and web-based frontend, the platform enables flexible multi-device test sessions, whether connected locally via USB or remotely through a USB-over-IP hub. These features significantly reduce certification time and allow engineers to execute tests without physically handling devices. Our study compares ODACE-RMS with traditional systems, which reduced engagement time in certification testing by 89%, significantly decreasing the need for human intervention and enhancing the overall efficiency of the certification process. Additionally, the proposed ODACE-RMS architecture results show that testing remotely is not much slower than testing locally through physical ports, even when multiple devices are tested in parallel, with an average 7% delay.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".