AndroR2: A Dataset of Manually-Reproduced Bug Reports for Android apps
This program is tentative and subject to change.
Software maintenance constitutes a large portion of the software development lifecycle. To carry out maintenance tasks, developers often need to understand and reproduce bug reports. As such, there has been increasing research activity coalescing around the notion of automating various activities related to bug reporting. A sizable portion of this research interest has focused on the domain of mobile apps. However, as research around mobile app bug reporting progresses, there is a clear need for a large, manually vetted, and reproducible set of real-world bug reports that can serve as a benchmark for future work. This paper presents AndroR2: a dataset of 90 manually reproduced bug reports for Android apps listed on Google Play and hosted on GitHub, systematically collected via an in-depth analysis of 459 reports extracted from the GitHub issue tracker. For each reproduced report, AndroR2 includes an apk file for the buggy version of the app, detailed reproduction steps, an executable reproduction script, and annotations on the quality of the reproduction steps associated with the original report. We believe that the AndroR2 dataset can be used to facilitate research in automatically analyzing, understanding, reproducing, localizing, and fixing bugs for mobile applications as well as other software maintenance activities more broadly in the future.
This program is tentative and subject to change.
Tue 18 May Times are displayed in time zone: Amsterdam, Berlin, Bern, Rome, Stockholm, Vienna change
17:00 - 17:50 | |||
17:00 8mTalk | What Code Is Deliberately Excluded from Test Coverage and Why? Technical Papers Andre HoraUFMG Pre-print | ||
17:08 8mTalk | AndroR2: A Dataset of Manually-Reproduced Bug Reports for Android apps Data Showcase Tyler WendlandUniversity of Minnesota, Jingyang SunUniversity of Bristish Columbia, Junayed MahmudGeorge Mason University, S M Hasan MansurGeorge Mason University, Steven HuangUniversity of Bristish Columbia, Kevin MoranGeorge Mason University, Julia RubinUniversity of British Columbia, Canada, Mattia FazziniUniversity of Minnesota | ||
17:16 8mTalk | Apache Software Foundation Incubator Project Sustainability Dataset Data Showcase Likang YinUniversity of California, Davis, Zhiyuan ZhangUniversity of California, Davis, Qi XuanInstitute of Cyberspace Security, Zhejiang University of Technology, Hangzhou 310023, China, Vladimir FilkovUniversity of California at Davis, USA | ||
17:25 8mTalk | Leveraging Models to Reduce Test Cases in Software Repositories Technical Papers Pre-print | ||
17:33 8mTalk | Which contributions count? Analysis of attribution in open source Technical Papers Jean-Gabriel YoungUniversity of Vermont, Amanda CasariOpen Source Programs Office, Google, Katie McLaughlinOpen Source Programs Office, Google, Milo TrujilloUniversity of Vermont, Laurent Hébert-DufresneUniversity of Vermont, James P. BagrowUniversity of Vermont Pre-print | ||
17:41 8mTalk | On Improving Deep Learning Trace Analysis with System Call Arguments Technical Papers Quentin FournierPolytechnique Montréal, Daniel AloisePolytechnique Montréal, Seyed Vahid AzhariCiena, François TetreaultCiena Pre-print |