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Title: ReCDroid+: Automated End-to-End Crash Reproduction from Bug Reports for Android Apps
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.  more » « less
Award ID(s):
2152340
PAR ID:
10417711
Author(s) / Creator(s):
; ; ; ; ; ; ;
Date Published:
Journal Name:
ACM Transactions on Software Engineering and Methodology
Volume:
31
Issue:
3
ISSN:
1049-331X
Page Range / eLocation ID:
1 to 33
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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