  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.
