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

Shidong Pan, Tianchen Guo, Lihong Zhang, Pei Liu, Zhenchang Xing, Xiaoyu Sun

TL;DR

This work conducts 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 and proposes a unified framework to detect incompatible APIs, especially for semantic changes.

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.

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

TL;DR

This work conducts 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 and proposes a unified framework to detect incompatible APIs, especially for semantic changes.

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.

Paper Structure

This paper contains 27 sections, 2 equations, 8 figures, 3 tables.

Table of Contents

  1. Introduction
  2. MOTIVATION
  3. Empirical Observation
  4. Problem Statement
  5. LLMs in Code Analysis
  6. Our Approach
  7. Information Extraction
  8. Signature Incompatible API Detection
  9. Semantic Incompatible API Detection
  10. Incompatibility Issue Detection
  11. Evaluation
  12. RQ1: Employing LLMs on Semantic Incompatible API Detection
  13. Benchmark Dataset
  14. Semantic Incompatible APIs
  15. RQ2: Statistic Analysis of Incompatible APIs
  16. ...and 12 more sections

Figures (8)

  • Figure 1: Status Quo of Android public APIs compared to previous version from level 4 to 33. The addition and removal are calculated based on the previous level.
  • Figure 2: The taxonomy of Incompatible APIs.
  • Figure 3: An overview of this study.
  • Figure 4: The design of prompt.
  • Figure 5: The distribution of code change types.
  • ...and 3 more figures