TY - JOUR
T1 - ReCDroid+
T2 - Automated End-To-End Crash Reproduction from Bug Reports for Android Apps
AU - Zhao, Yu
AU - Su, Ting
AU - Liu, Yang
AU - Zheng, Wei
AU - Wu, Xiaoxue
AU - Kavuluru, Ramakanth
AU - Halfond, William G.J.
AU - Yu, Tingting
N1 - Publisher Copyright:
© 2022 Association for Computing Machinery.
PY - 2022/7
Y1 - 2022/7
N2 - The large demand of mobile devices creates significant concerns about the quality of mobile applications (apps). Developers heavily rely on bug reports in issue tracking systems to reproduce failures (e.g., crashes). However, the process of crash reproduction is often manually done by developers, making the resolution of bugs inefficient, especially given that bug reports are often written in natural language. To improve the productivity of developers in resolving bug reports, in this paper, we introduce a novel approach, called ReCDroid+, that can automatically reproduce crashes from bug reports for Android apps. ReCDroid+ uses a combination of natural language processing (NLP), deep learning, and dynamic GUI exploration to synthesize event sequences with the goal of reproducing the reported crash. We have evaluated ReCDroid+ on 66 original bug reports from 37 Android apps. The results show that ReCDroid+ successfully reproduced 42 crashes (63.6% success rate) directly from the textual description of the manually reproduced bug reports. A user study involving 12 participants demonstrates that ReCDroid+ can improve the productivity of developers when resolving crash bug reports.
AB - The large demand of mobile devices creates significant concerns about the quality of mobile applications (apps). Developers heavily rely on bug reports in issue tracking systems to reproduce failures (e.g., crashes). However, the process of crash reproduction is often manually done by developers, making the resolution of bugs inefficient, especially given that bug reports are often written in natural language. To improve the productivity of developers in resolving bug reports, in this paper, we introduce a novel approach, called ReCDroid+, that can automatically reproduce crashes from bug reports for Android apps. ReCDroid+ uses a combination of natural language processing (NLP), deep learning, and dynamic GUI exploration to synthesize event sequences with the goal of reproducing the reported crash. We have evaluated ReCDroid+ on 66 original bug reports from 37 Android apps. The results show that ReCDroid+ successfully reproduced 42 crashes (63.6% success rate) directly from the textual description of the manually reproduced bug reports. A user study involving 12 participants demonstrates that ReCDroid+ can improve the productivity of developers when resolving crash bug reports.
KW - Android GUI testing
KW - Bug report
KW - bug reproducing
UR - https://www.scopus.com/pages/publications/85130693930
U2 - 10.1145/3488244
DO - 10.1145/3488244
M3 - 文章
AN - SCOPUS:85130693930
SN - 1049-331X
VL - 31
JO - ACM Transactions on Software Engineering and Methodology
JF - ACM Transactions on Software Engineering and Methodology
IS - 3
M1 - 36
ER -