This study conducts a comparative analysis of the performance of different programming languages on Android embedded systems, focusing on execution time. Through controlled experiments, we compared traditional and emerging programming languages (Java, Kotlin, C, C++, Python, Golang, and Rust) using a variety of representative algorithms. The results revealed significant disparities among the programming languages, with C and C++ performing significantly better than the others, although in some algorithms Golang approached close in terms of execution time. The native Android application languages, Java and Kotlin, showed intermediate results compared to the others, with very close outcomes between them. Python and Rust had the worst results, with Python's performance being justified by the interpreted nature of the language and the lack of multiprocessing features on Android. Rust was possibly hindered by its inability to efficiently access low-level resources when applied to the Android application layer.

The analysis of memory consumption is important for the stability and performance of Android applications. This work presents a method to extract and visualize memory-related data from Android bug report files, focusing on improving the debugging process for potential memory issues. The proposed method utilizes regular expressions for extracting key memory metrics and presents this data through interactive visualizations, aiding developers in identifying memory performance issues. We demonstrate the use of this visualization approach through an experimental analysis involving a high memory pressure scenario. This scenario allowed us to monitor memory consumption over time and analyze the causes of process terminations under this stress condition. Future work will involve evaluating the method's effectiveness under varying memory pressure scenarios, using the same or similar applications, and integrating advanced learning techniques for log analysis to capture complex patterns related to memory issues, ultimately helping to optimize system performance.