TY - GEN
T1 - ReCDroid
T2 - 41st IEEE/ACM International Conference on Software Engineering, ICSE 2019
AU - Zhao, Yu
AU - Yu, Tingting
AU - Su, Ting
AU - Liu, Yang
AU - Zheng, Wei
AU - Zhang, Jingzhi
AU - Halfond, William
N1 - Publisher Copyright:
© 2019 IEEE.
PY - 2019/5
Y1 - 2019/5
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 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) and dynamic GUI exploration to synthesize event sequences with the goal of reproducing the reported crash. We have evaluated ReCDroid on 51 original bug reports from 33 Android apps. The results show that ReCDroid successfully reproduced 33 crashes (63.5% success rate) directly from the textual description of 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 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) and dynamic GUI exploration to synthesize event sequences with the goal of reproducing the reported crash. We have evaluated ReCDroid on 51 original bug reports from 33 Android apps. The results show that ReCDroid successfully reproduced 33 crashes (63.5% success rate) directly from the textual description of bug reports. A user study involving 12 participants demonstrates that ReCDroid can improve the productivity of developers when resolving crash bug reports.
KW - Android
KW - Bug reproduction
KW - Natural language processing
UR - https://www.scopus.com/pages/publications/85068255207
U2 - 10.1109/ICSE.2019.00030
DO - 10.1109/ICSE.2019.00030
M3 - 会议稿件
AN - SCOPUS:85068255207
T3 - Proceedings - International Conference on Software Engineering
SP - 128
EP - 139
BT - Proceedings - 2019 IEEE/ACM 41st International Conference on Software Engineering, ICSE 2019
PB - IEEE Computer Society
Y2 - 25 May 2019 through 31 May 2019
ER -