Feedback-Driven Automated Whole Bug Report Reproduction for Android Apps
This program is tentative and subject to change.
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, 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 bug reports. Our evaluation of 96 Android bug reports (73 crash and 23 non-crash) demonstrates that ReBL successfully reproduced 90.61% of these reports, averaging only 74.98 seconds per bug report. Additionally, ReBL outperformed three existing tools in both success rate and speed.
This program is tentative and subject to change.
Fri 20 SepDisplayed time zone: Amsterdam, Berlin, Bern, Rome, Stockholm, Vienna change
10:30 - 11:50 | |||
10:30 20mTalk | Atlas: Automating Cross-Language Fuzzing on Android Closed-Source Libraries Technical Papers Hao Xiong Zhejiang University, Qinming Dai Zhejiang University, Rui Chang Zhejiang University, Mingran Qiu Zhejiang University, Renxiang Wang Zhejiang University, Wenbo Shen Zhejing University, Yajin Zhou Zhejiang University DOI | ||
10:50 20mTalk | Feedback-Driven Automated Whole Bug Report Reproduction for Android Apps Technical Papers Dingbang Wang University of Connecticut, Yu Zhao University of Central Missouri, Sidong Feng Monash University, Zhaoxu Zhang University of Southern California, William G.J. Halfond University of Southern California, Chunyang Chen Technical University of Munich (TUM), Xiaoxia Sun China Mobile (Suzhou) Software Technology Co., Ltd., Jiangfan Shi , Tingting Yu University of Connecticut | ||
11:10 20mTalk | NativeSummary: Summarizing Native Binary Code for Inter-language Static Analysis of Android Apps Technical Papers Jikai Wang Huazhong University of Science and Technology, Haoyu Wang Huazhong University of Science and Technology | ||
11:30 20mTalk | Towards Automatic Oracle Prediction for AR testing: Assessing Virtual Object Placement Quality under Real-world Scenes Technical Papers Xiaoyi Yang Rochester Institute of Technology, Yuxing Wang Rochester Institute of Technology, Tahmid Rafi University of Texas at San Antonio, Dongfang Liu Rochester Institute of Technology, Xiaoyin Wang University of Texas at San Antonio, Xueling Zhang Rochester Institute of Technology | ||