LadyBug: A GitHub Bot for UI-Enhanced Bug Localization in Mobile Apps

TL;DR

LadyBug is an innovative GitHub bot that enhances bug localization in Android apps by integrating UI interaction data with text retrieval, significantly improving accuracy over traditional methods.

Contribution

The paper presents LadyBug, a novel GitHub bot that combines UI information with text data to improve bug localization in mobile apps, which is a new approach in this domain.

Findings

LadyBug outperforms text-retrieval baselines in bug localization accuracy.

Utilizing UI information substantially increases localization precision.

The tool is open-source and validated on 80 bug reports from 39 Android apps.

Abstract

This paper introduces LadyBug, a GitHub bot that automatically localizes bugs for Android apps by combining UI interaction information with text retrieval. LadyBug connects to an Android app's GitHub repository, and is triggered when a bug is reported in the corresponding issue tracker. Developers can then record a reproduction trace for the bug on a device or emulator and upload the trace to LadyBug via the GitHub issue tracker. This enables LadyBug to utilize both the text from the original bug description, and UI information from the reproduction trace to accurately retrieve a ranked list of files from the project that most likely contain the reported bug. We empirically evaluated LadyBug using an automated testing pipeline and benchmark called RedWing that contains 80 fully-localized and reproducible bug reports from 39 Android apps. Our results illustrate that LadyBug outperforms text-retrieval-based baselines and that the utilization of UI information leads to a substantial increase in localization accuracy. LadyBug is an open-source tool, available at https://github.com/LadyBugML/ladybug. A video showing the capabilities of Ladybug can be viewed here: https://youtu.be/hI3tzbRK0Cw