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Android has become the predominant smartphone operating system, with arapidly evolving ecosystem that requires app developers to frequently updatetheir apps to maintain quality, security, and compatibility. While deeplearning has made significant strides in various software engineering tasks,including automated code updates, existing methods are not specificallytailored for Android apps, and the potential of pre-trained Language Models ofCode (CodeLMs) for updating Android app code remains unexplored. In this paper,we present the first comprehensive evaluation of state-of-the-art CodeLMs,including CodeT5, CodeBERT, CodeGPT, and UniXcoder, for recommending codeupdates in Android applications. To facilitate this evaluation, we curate aunique dataset of paired updated methods from 3,195 Android apps published onGoogle Play and hosted on GitHub between 2008 and 2022. Our findingsdemonstrate that pre-trained CodeLMs outperform traditional approaches,achieving a higher accuracy ranging from 190% to 385% under a realistictime-wise evaluation scenario. Among the CodeLMs, CodeT5 consistently exhibitssuperior performance across most code update types. Furthermore, we examine theimpact of update types, evaluation scenarios, method size, and update size onthe performance of CodeLMs, revealing areas for future research to improvetemporal adaptability and generalization capabilities.

AutoUpdate: Automatically Recommend Code Updates for Android Apps