# V2S: A Tool for Translating Video Recordings of Mobile App Usages into Replayable Scenarios

arXiv: https://arxiv.org/abs/2103.04531 · HTML: https://mlsanity.com/paper/2103.04531/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Mar 2021.

## Built on by (most cited first, 3 of 3)

- [Combining Language and App UI Analysis for the Automated Assessment of Bug Reproduction Steps](https://mlsanity.com/paper/2502.04251/) (2025)
- [MotorEase: Automated Detection of Motor Impairment Accessibility Issues in Mobile App UIs](https://mlsanity.com/paper/2403.13690/) (2024)
- [AURORA: Navigating UI Tarpits via Automated Neural Screen Understanding](https://mlsanity.com/paper/2404.01240/) (2024)

## Abstract (arXiv)

Screen recordings are becoming increasingly important as rich software artifacts that inform mobile application development processes. However, the amount of manual effort required to extract information from these graphical artifacts can hinder resource-constrained mobile developers. This paper presents Video2Scenario (V2S), an automated tool that processes video recordings of Android app usages, utilizes neural object detection and image classification techniques to classify the depicted user actions, and translates these actions into a replayable scenario. We conducted a comprehensive evaluation to demonstrate V2S's ability to reproduce recorded scenarios across a range of devices and a diverse set of usage cases and applications. The results indicate that, based on its performance with 175 videos depicting 3,534 GUI-based actions, V2S is accurate in reproducing ≈89% of actions from collected videos.