This article outlines an extensive methodology for assembling high-quality datasets vital for training machine learning models. It focuses on Android API collections, categorized based on usability, frequency, and implementation complexity, culminating in 20 selected APIs from Android's core functionalities and popular applications. The research excludes sensitive tasks to ensure developers can execute functions responsibly and within user-permission boundaries. Additional vehicle APIs were also considered, showcasing a broader dataset potential for improving model training and validation processes, supported by appendices with relevant examples.
The methodology for gathering superior datasets emphasizes usability, frequency, and complexity, ultimately yielding 20 Android APIs categorized for efficient developer execution.
Key API functionalities for Android development include system calls, app-level operations, and user-permission validation, excluding sensitive tasks that might compromise user privacy.
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