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.
@article{arxiv.2407.05165,
title = {Feedback-Driven Automated Whole Bug Report Reproduction for Android Apps},
author = {Dingbang Wang and Yu Zhao and Sidong Feng and Zhaoxu Zhang and William G. J. Halfond and Chunyang Chen and Xiaoxia Sun and Jiangfan Shi and Tingting Yu},
journal= {arXiv preprint arXiv:2407.05165},
year = {2024}
}