poster

Guiding the crowds for Android testing

Authors:

Xin Zhang,

Zhenyu Chen,

Chunrong Fang,

Zicong LiuAuthors Info & Claims

ICSE '16: Proceedings of the 38th International Conference on Software Engineering Companion

Pages 752 - 753

https://doi.org/10.1145/2889160.2892659

Published: 14 May 2016 Publication History

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Abstract

Crowdsourced testing is an emerging trend in software testing, especially for mobile testing due to Android fragment issues. Gaining the productive outputs of high quality always comes with the assumption that the workers are experienced. However, the crowd of testers are not usually professionals. Thus, it becomes an interesting topic to guide the crowd to obtain domain knowledge and fulfill tasks. Motivated by an article in Science[1], we propose an approach in this paper to enhance 'the power of the crowd'. (1) Along with professionals taking the task, exception information is recorded into database to provide hints for the crowd.(2) With the feedback from new exceptions caught in processing of crowd, the database is enriched. Such an iterative process will guide the crowd to finish their tasks effectively.

References

[1]

Pietro Micheluccil and Janis L. Dickinson. Computing power of crowds. Science 351(6258):37--39, 2015.

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[2]

Di Liu, Randolph G Bias, Matthew Lease, and Rebecca Kuipers. Crowdsourcing for usability testing. Proceedings of the American Society for Information Science and Technology, 49(1): 1--10, 2012.

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[3]

Aniket Kittur, Jeffrey V Nickerson, Michael Bernstein, Elizabeth Gerber, Aaron Shaw, John Zimmerman, Matt Lease, and John Horton. The future of crowd work Proceedings of the 2013 conference on Computer supported cooperative work. 1301--1318, 2013.

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[4]

Shkodran Zogaj, Ulrich Bretschneider, and Jan Marco Leimeister. Managing crowdsourced software testing: a case study based insight on the challenges of a crowdsourcing intermediary. Journal of Business Economics, 84(3): 375--405, 2014.

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Cited By

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Khan HAli FGhadi YNazir SUllah IMohamed H(2023)Human–Computer Interaction and Participation in Software CrowdsourcingElectronics10.3390/electronics1204093412:4(934)Online publication date: 13-Feb-2023
https://doi.org/10.3390/electronics12040934
Zhong YShi MXu YFang CChen Z(2022)Iterative Android automated testingFrontiers of Computer Science10.1007/s11704-022-1658-817:5Online publication date: 24-Dec-2022
https://doi.org/10.1007/s11704-022-1658-8
Wang GAli FYang JNazir SYang TKhan AImtiaz M(2021)Multicriteria-Based Crowd Selection Using Ant Colony OptimizationComplexity10.1155/2021/66222312021(1-11)Online publication date: 22-Jan-2021
https://doi.org/10.1155/2021/6622231
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Index Terms

Guiding the crowds for Android testing
1. Human-centered computing
  1. Human computer interaction (HCI)
2. Software and its engineering
  1. Software creation and management
    1. Software verification and validation
      1. Software defect analysis
        Software testing and debugging

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Information & Contributors

Information

Published In

ICSE '16: Proceedings of the 38th International Conference on Software Engineering Companion

May 2016

946 pages

ISBN:9781450342056

DOI:10.1145/2889160

General Chair:
Laura Dillon
Michigan State University
,
Program Chairs:
Willem Visser
Stellenbosch University, South Africa
,
Laurie Williams
North Carolina State University

Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 14 May 2016

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Poster

Funding Sources

the National Natural Science Foundation of China
the Program B for Outstanding PhD candidate of Nanjing Univerisity

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ICSE '16

Sponsor:

ACM
SIGSOFT
IEEE-CS\TCSE
IEEE-CS\DATC

ICSE '16: 38th International Conference on Software Engineering

May 14 - 22, 2016

Texas, Austin

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Overall Acceptance Rate 276 of 1,856 submissions, 15%

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Cited By

View all

Khan HAli FGhadi YNazir SUllah IMohamed H(2023)Human–Computer Interaction and Participation in Software CrowdsourcingElectronics10.3390/electronics1204093412:4(934)Online publication date: 13-Feb-2023
https://doi.org/10.3390/electronics12040934
Zhong YShi MXu YFang CChen Z(2022)Iterative Android automated testingFrontiers of Computer Science10.1007/s11704-022-1658-817:5Online publication date: 24-Dec-2022
https://doi.org/10.1007/s11704-022-1658-8
Wang GAli FYang JNazir SYang TKhan AImtiaz M(2021)Multicriteria-Based Crowd Selection Using Ant Colony OptimizationComplexity10.1155/2021/66222312021(1-11)Online publication date: 22-Jan-2021
https://doi.org/10.1155/2021/6622231
Chen XJiang HLi XNie LYu DHe TChen Z(2020)A systemic framework for crowdsourced test report quality assessmentEmpirical Software Engineering10.1007/s10664-019-09793-8Online publication date: 27-Feb-2020
https://doi.org/10.1007/s10664-019-09793-8
Li HFang CWei ZChen ZZhang DMøller A(2019)CoCoTest: collaborative crowdsourced testing for Android applicationsProceedings of the 28th ACM SIGSOFT International Symposium on Software Testing and Analysis10.1145/3293882.3339000(390-393)Online publication date: 10-Jul-2019
https://dl.acm.org/doi/10.1145/3293882.3339000
Jiang BZhang YChan WZhang Z(2019)A Systematic Study on Factors Impacting GUI Traversal-Based Test Case Generation Techniques for Android ApplicationsIEEE Transactions on Reliability10.1109/TR.2019.292845968:3(913-926)Online publication date: Sep-2019
https://doi.org/10.1109/TR.2019.2928459

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CoCoTest: collaborative crowdsourced testing for Android applications

Personas and redundancies in crowdsourced testing

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