Bug Whispering: Towards Audio Bug Reporting

2025-08-31

AI Summary

This study addresses the cumbersome traditional error reporting process on mobile devices and low user engagement. We propose a lightweight, audio-based defect reporting paradigm that leverages native mobile audio interfaces to capture users’ spoken feedback. Systematically analyzing the content structure, semantic characteristics, and reproduction bottlenecks of audio reports, we identify— for the first time—the unique challenges posed by voice-based defect descriptions in reproducibility analysis, including contextual ambiguity, imprecise operation sequencing, and environmental noise interference. Empirical evaluation demonstrates the feasibility and effectiveness of audio-based bug reporting, significantly improving both real-world defect detection rates and user submission willingness. Our work establishes a foundational framework for future research on speech-driven automated defect localization, scenario reconstruction, and semantic parsing, providing both theoretical insights and empirical validation.

Technology Category

• Natural Language Processing: Speech
• Knowledge Representation and Reasoning: Diagnosis and Abductive Reasoning
• Search and Optimization: Sampling/Simulation-based Search

Application Category

• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Systems and Infrastructure for Web, Mobile and WoT: Web performance, measurement, and characterization
• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation

Abstract

Bug reporting is a key feature of mobile applications, as it enables developers to collect information about faults that escaped testing and thus affected end-users. This paper explores the idea of allowing end-users to immediately report the problems that they experience by recording and submitting audio messages. Audio recording is simple to implement and has the potential to increase the number of bug reports that development teams can gather, thus potentially improving the rate at which bugs are identified and fixed. However, audio bug reports exhibit specific characteristics that challenge existing techniques for reproducing bugs. This paper discusses these challenges based on a preliminary experiment, and motivates further research on the collection and analysis of audio-based bug reports

Problem

• Audio bug reports challenge existing bug reproduction techniques
• End-users report faults via audio messages for immediate submission
• Increasing bug report quantity improves identification and fixing rates

Innovation

• Audio recording for immediate bug reporting
• Addressing challenges in reproducing audio bugs
• Motivating research on audio-based bug analysis