BugRepro: Enhancing Android Bug Reproduction with Domain-Specific Knowledge Integration

• URL: http://arxiv.org/abs/2505.14528v2
• Date: Thu, 29 May 2025 13:03:01 GMT
• Title: BugRepro: Enhancing Android Bug Reproduction with Domain-Specific Knowledge Integration
• Authors: Hongrong Yin, Jinhong Huang, Yao Li, Yunwei Dong, Tao Zhang,
• Abstract summary: BugRepro is a novel technique that integrates domain-specific knowledge to enhance the accuracy and efficiency of bug reproduction.<n>BugRepro significantly outperforms two state-of-the-art methods.
• Score: 4.833035081314386
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Mobile application development is a fast-paced process where maintaining high-quality user experiences is crucial. Bug reproduction, a key aspect of maintaining app quality, often faces significant challenges. Specifically, when descriptions in bug reports are ambiguous or difficult to comprehend, current approaches fail to extract accurate information. Moreover, modern applications exhibit inherent complexity with multiple pages and diverse functionalities, making it challenging for existing methods to map the relevant information in bug reports to the corresponding UI elements that need to be manipulated. To address these challenges, we propose BugRepro, a novel technique that integrates domain-specific knowledge to enhance the accuracy and efficiency of bug reproduction. BugRepro adopts a Retrieval-Augmented Generation (RAG) approach. It retrieves similar bug reports along with their corresponding steps to reproduce (S2R) entities from an example-rich RAG document. In addition, BugRepro explores the graphical user interface (GUI) of the app and extracts transition graphs from the user interface to incorporate app-specific knowledge to guide large language models (LLMs) in their exploration process. Our experiments demonstrate that BugRepro significantly outperforms two state-of-the-art methods (ReCDroid and AdbGPT). For S2R entity extraction accuracy, it achieves a 7.57 to 28.89 percentage point increase over prior methods. For the bug reproduction success rate, the improvement reaches 74.55% and 152.63%. In reproduction efficiency, the gains are 0.72% and 76.68%.

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