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Feedback-Driven Automated Whole Bug Report Reproduction for Android Apps

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arxiv 2407.05165 v3 pith:X2LFWCQL submitted 2024-07-06 cs.SE

Dingbang Wang , Yu Zhao , Sidong Feng , Zhaoxu Zhang , William G. J. Halfond , Chunyang Chen , Xiaoxia Sun , Jiangfan Shi
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Tingting Yu
This is my paper · ORCID
classification cs.SE
keywords reblreportsreportandroidapproachcrashfeedback-drivenhandling
verification ladder T0 review T1 audit T2 compute T3 formal

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In software development, bug report reproduction is a challenging task. This paper introduces ReBL, a novel feedback-driven approach that leverages GPT-4, a large-scale language model (LLM), to automatically reproduce Android bug reports. Unlike traditional methods, ReBL bypasses the use of Step to Reproduce (S2R) entities. Instead, it leverages the entire textual bug report and employs innovative prompts to enhance GPT's contextual reasoning. This approach is more flexible and context-aware than the traditional step-by-step entity matching approach, resulting in improved accuracy and effectiveness. In addition to handling crash reports, ReBL has the capability of handling non-crash functional bug reports. Our evaluation of 96 Android bug reports (73 crash and 23 non-crash) demonstrates that ReBL successfully reproduced 90.63% of these reports, averaging only 74.98 seconds per bug report. Additionally, ReBL outperformed three existing tools in both success rate and speed.

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Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. AEGIS: An Agent-based Framework for General Bug Reproduction from Issue Descriptions

    cs.SE 2024-11 conditional novelty 6.0 of 10

    AEGIS combines concise context extraction with finite-state-machine feedback control to make LLM agents reproduce more software bugs from issue descriptions than existing baselines.

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