ReCDroid+: Automated End-To-End Crash Reproduction from Bug Reports for Android Apps

Yu Zhao, Ting Su, Yang Liu, Wei Zheng, Xiaoxue Wu, Ramakanth Kavuluru, William G.J. Halfond, Tingting Yu

Research output: Contribution to journalArticlepeer-review

22 Scopus citations

Abstract

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.

Original languageEnglish
Article number36
JournalACM Transactions on Software Engineering and Methodology
Volume31
Issue number3
DOIs
StatePublished - Jul 2022

Bibliographical note

Publisher Copyright:
© 2022 Association for Computing Machinery.

Keywords

  • Android GUI testing
  • Bug report
  • bug reproducing

ASJC Scopus subject areas

  • Software

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