Toward a non-invasive robotic-supported approach for data loss detection in android applications

Context: Maintaining data consistency in mobile applications is essential, as activity restarts—such as those triggered by screen rotations—can lead to user data loss. Existing automated approaches, including DLD and R-DLD, rely on semi-invasive testing methods that require system-level access through debugging tools like ADB or app instrumentation. These dependencies limit their applicability to specific devices and reduce realism in user interaction. This work aims to overcome the limitations of semi-invasive data loss detection by introducing a fully non-invasive, hardware-based framework for Android applications. The goal is to preserve the authenticity of user interaction while maintaining detection accuracy comparable to existing approaches. We present a new version of R-DLD, which leverages an industrial robotic arm to reproduce realistic user interactions such as touch gestures, scrolling, and device rotations without system-level access. The oracle was completely redesigned, using an external camera and computer vision techniques that apply similarity and dissimilarity metrics to identify data loss after double rotation events. Experimental evaluation on thirty benchmark Android applications showed that the new R-DLD detected 100% of known data loss cases, matching R-DLD’s performance while operating entirely non-invasively. The results also demonstrate the robustness of the visual oracle against environmental lighting variations. The new R-DLD represents a significant advancement in realistic, non-invasive testing for mobile applications. By eliminating the need for ADB or instrumentation, it broadens the applicability of automated data loss detection and establishes a foundation for future research in vision-based, robotics-supported testing. • Fully non-invasive robotic framework for Android data loss detection. • External camera and vision-based oracle replace system-level access. • Industrial robot reproduces realistic touch and rotation interactions. • Visual metrics accurately detect data loss after double rotation events. • Achieves 100% detection of known data loss cases on real applications.