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. Objective: 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. Method: 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. Results: 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. Conclusion: 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.