The article presents a comparative analysis of the effectiveness of the Firebase Test Lab cloud platform for mobile application testing within the context of the fragmented Android ecosystem. The study focuses on evaluating the platform's performance, defect detection accuracy, and cost-effectiveness compared to traditional testing methods. The methodology incorporates automated testing (Espresso, XCTest) for UI regression checks, interactive exploratory testing with user action recording, and resource consumption monitoring (CPU, RAM, GPU) to identify hard-to-detect defects. Results demonstrate a 30-40% reduction in testing time through parallel test execution, enhanced analytics (heat maps, session recordings), and high accuracy in detecting ergonomic interface issues. The practical significance of the research lies in developing recommendations for optimizing testing processes and making informed choices regarding mobile application quality assurance tools.
The article presents a comparative analysis of the effectiveness of the Firebase Test Lab cloud platform for mobile application testing within the context of the fragmented Android ecosystem. The study focuses on evaluating the platform's performance, defect detection accuracy, and cost-effectiveness compared to traditional testing methods. The methodology incorporates automated testing (Espresso, XCTest) for UI regression checks, interactive exploratory testing with user action recording, and resource consumption monitoring (CPU, RAM, GPU) to identify hard-to-detect defects. Results demonstrate a 30-40% reduction in testing time through parallel test execution, enhanced analytics (heat maps, session recordings), and high accuracy in detecting ergonomic interface issues. The practical significance of the research lies in developing recommendations for optimizing testing processes and making informed choices regarding mobile application quality assurance tools.