LDMDroid: Leveraging LLMs for Detecting Data Manipulation Errors in Android Apps

TL;DR

LDMDroid is an automated Android testing framework leveraging LLMs to detect data manipulation errors by generating effective UI event sequences and analyzing data state changes.

Contribution

It introduces a novel LLM-guided approach for improving DMF testing coverage and accuracy in Android apps, with a publicly available implementation.

Findings

LDMDroid achieved higher DMF triggering success rates than baseline tools.

Discovered 17 unique data manipulation bugs in real-world apps.

14 bugs were confirmed by developers and 11 were fixed.

Abstract

Android apps rely heavily on Data Manipulation Functionalities (DMFs) for handling app-specific data through CRUDS operations, making their correctness vital for reliability. However, detecting Data Manipulation Errors (DMEs) is challenging due to their dependence on specific UI interaction sequences and manifestation as logic bugs. Existing automated UI testing tools face two primary challenges: insufficient UI path coverage for adequate DMF triggering and reliance on manually written test scripts. To address these issues, we propose an automated approach using Large Language Models (LLMs) for DME detection. We developed LDMDroid, an automated UI testing framework for Android apps. LDMDroid enhances DMF triggering success by guiding LLMs through a state-aware process for generating UI event sequences. It also uses visual features to identify changes in data states, improving DME verification accuracy. We evaluated LDMDroid on 24 real-world Android apps, demonstrating improved DMF triggering success rates compared to baselines. LDMDroid discovered 17 unique bugs, with 14 confirmed by developers and 11 fixed. The tool is publicly available at https://github.com/runnnnnner200/LDMDroid.