short-paper

Strategies for Crowdworkers to Overcome Barriers in Competition-based Software Crowdsourcing Development

Authors:

Alexandre Zanatta,

Leticia Machado,

Igor Steinmacher,

Rafael Prikladnicki,

Cleidson R. B. de SouzaAuthors Info & Claims

ICSEW'20: Proceedings of the IEEE/ACM 42nd International Conference on Software Engineering Workshops

Pages 125 - 128

https://doi.org/10.1145/3387940.3392243

Published: 25 September 2020 Publication History

Abstract

Crowdsourcing in software development uses a large pool of developers on-demand to outsource parts or the entire software project to a crowd. To succeed, this requires a continuous influx of developers, or simply crowdworkers. However, crowdworkers face many barriers when attempting to participate in software crowdsourcing. Often, these barriers lead to a low number and poor quality of submitted solutions. In our previous work, we identified several barriers faced by crowdworkers including finding a task according to his/her abilities, setting up the environment to perform the task, and managing one's personal time. We also proposed six strategies to overcome or minimize these barriers. In this paper, these six strategies are evaluated questioning Software Crowdsourcing (SW CS) experts. The results show that software crowdsourcing needs to: (i) provide a system that helps matching tasks requirements and crowdworker's profile; (ii) adopt containers or virtual machines to help crowdworkers set up their environment to perform the task, (iii) plan and control crowdworkers' personal time, and (iv) adopt communication channels to allow crowdworkers to clarify questions about the requirements and, as a consequence, finish the tasks.

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Cited By

Yasmin AHaider WDaud ABanjar A(2024)Machine learning based software effort estimation using development-centric features for crowdsourcing platformIntelligent Data Analysis10.3233/IDA-23736628:1(299-329)Online publication date: 3-Feb-2024
https://doi.org/10.3233/IDA-237366

Index Terms

Strategies for Crowdworkers to Overcome Barriers in Competition-based Software Crowdsourcing Development
1. Software and its engineering
  1. Software creation and management
    1. Collaboration in software development

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cover image ACM Conferences

ICSEW'20: Proceedings of the IEEE/ACM 42nd International Conference on Software Engineering Workshops

June 2020

831 pages

ISBN:9781450379632

DOI:10.1145/3387940

Copyright © 2020 ACM.

Permission to make digital or hard copies of all or part 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 components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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SIGSOFT: ACM Special Interest Group on Software Engineering
KIISE: Korean Institute of Information Scientists and Engineers
IEEE CS

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New York, NY, United States

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Published: 25 September 2020

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ICSE '20

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ICSE '20: 42nd International Conference on Software Engineering

June 27 - July 19, 2020

Seoul, Republic of Korea

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Cited By

Yasmin AHaider WDaud ABanjar A(2024)Machine learning based software effort estimation using development-centric features for crowdsourcing platformIntelligent Data Analysis10.3233/IDA-23736628:1(299-329)Online publication date: 3-Feb-2024
https://doi.org/10.3233/IDA-237366

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