A Large-scale Investigation of Semantically Incompatible APIs behind Compatibility Issues in Android Apps

TL;DR

This paper presents a large-scale study on semantically incompatible APIs in Android, using static analysis and large language models to improve detection of compatibility issues across versions.

Contribution

It introduces a unified framework leveraging static analysis and LLMs to detect semantic API incompatibilities, significantly expanding the scope of previous methods.

Findings

Achieves 0.83 F1-score in detecting incompatible APIs.

Detects 5,481 incompatible APIs from Android versions 4 to 33.

Improves detection coverage by 92.3% over existing methods.

Abstract

Application Programming Interface (API) incompatibility is a long-standing issue in Android application development. The rapid evolution of Android APIs results in a significant number of API additions, removals, and changes between adjacent versions. Unfortunately, this high frequency of alterations may lead to compatibility issues, often without adequate notification to developers regarding these changes. Although researchers have proposed some work on detecting compatibility issues caused by changes in API signatures, they often overlook compatibility issues stemming from sophisticated semantic changes. In response to this challenge, we conducted a large-scale discovery of incompatible APIs in the Android Open Source Project (AOSP) by leveraging static analysis and pre-trained Large Language Models (LLMs) across adjacent versions. We systematically formulate the problem and propose a unified framework to detect incompatible APIs, especially for semantic changes. It's worth highlighting that our approach achieves a 0.83 F1-score in identifying semantically incompatible APIs in the Android framework. Ultimately, our approach detects 5,481 incompatible APIs spanning from version 4 to version 33. We further demonstrate its effectiveness in supplementing the state-of-the-art methods in detecting a broader spectrum of compatibility issues (+92.3%) that have been previously overlooked.