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Screen recordings of mobile applications are easy to obtain and capture awealth of information pertinent to software developers (e.g., bugs or featurerequests), making them a popular mechanism for crowdsourced app feedback. Thus,these videos are becoming a common artifact that developers must manage. Inlight of unique mobile development constraints, including swift release cyclesand rapidly evolving platforms, automated techniques for analyzing all types ofrich software artifacts provide benefit to mobile developers. Unfortunately,automatically analyzing screen recordings presents serious challenges, due totheir graphical nature, compared to other types of (textual) artifacts. Toaddress these challenges, this paper introduces V2S+, an automated approach fortranslating video recordings of Android app usages into replayable scenarios.V2S+ is based primarily on computer vision techniques and adapts recentsolutions for object detection and image classification to detect and classifyuser gestures captured in a video, and convert these into a replayable testscenario. Given that V2S+ takes a computer vision-based approach, it isapplicable to both hybrid and native Android applications. We performed anextensive evaluation of V2S+ involving 243 videos depicting 4,028 GUI-basedactions collected from users exercising features and reproducing bugs from acollection of over 90 popular native and hybrid Android apps. Our resultsillustrate that V2S+ can accurately replay scenarios from screen recordings,and is capable of reproducing $\approx$ 90.2% of sequential actions recorded innative application scenarios on physical devices, and $\approx$ 83% ofsequential actions recorded in hybrid application scenarios on emulators, bothwith low overhead. A case study with three industrial partners illustrates thepotential usefulness of V2S+ from the viewpoint of developers.

Translating Video Recordings of Complex Mobile App UI Gestures into Replayable Scenarios