Crowdsourcing Software Development - a possible path?
Resumo
Context: Crowdsourcing software development (CSSD) is a form of collective intelligence that is gaining attention in the Information Technology industry as a work alternative for software projects. CSSD represents a paradigm shift in the in-house development approach, promising deliveries with quality, productivity and innovation. Problem: CSSD is an emerging approach, both in research and in practice. Despite the existence of commercial supporting platforms, organizations of different sizes still face the challenge of how to organize the collective work, and a clear definition of how to perform CSSD is not known yet. Solution: This article presents an analysis of the literature to identify the main processes, practices, tools and platforms used in CSSD initiatives, and to understand the benefits and challenges reported in these initiatives as well. IS theory: Not applicable. Method: Descriptive research based on systematic literature mapping. Summary of Results: CSSD is understood as a democratic alternative with great potential for improving quality, productivity and innovation in software projects. However, challenges for its realization are many and of different kinds (technical, managerial, methodological and legal). Results also show not a large amount of CSSD case study reports in real situations. Contributions and Impact on the IS area: This work provides an overview of CSSD application, as well as a critical analysis of its advantages and challenges, contributing with discussions to the challenge of Information Systems and the Open World. This overview enables reflection on the adoption of CSSD, stimulating the research of solutions under development by crowds for companies of different sizes.
Referências
Izzat Alsmadi and Saqib Saeed. 2013. Toward agile interactive software development process models for crowd source projects. 199–213 pages. cited By 0.
Talat Ambreen and Naveed Ikram. 2016. A State-of-the-Art of Empirical Literature of Crowdsourcing in Computing. In 2016 IEEE 11th International Conference on Global Software Engineering (ICGSE). 189–190.
Ricardo de A. Falbo. 2018. ”Mapeamento sistemático”. Retrieved April 04, 2022 from http://claudiaboeres.pbworks.com/w/file/fetch/133747116/Mapeament
Renata Mendes de Araujo. 2017. Sistemas de Informação e os Desafios do Mundo Aberto. In: Boscarioli, C.; Araujo, R. M.; Maciel, R. S. P. “I GranDSI-BR – Grand Research Challenges in Information Systems in Brazil 2016-2026”. Special Committee on Information Systems (CE-SI). Brazilian Computer Society (SBC). ISBN: [978-85-7669-384-0]. 2017. 184p.(2017). https://doi.org/10.5753/sbc.2884.0
Alpana Dubey, Kumar Abhinav, Sakshi Taneja, Gurdeep Virdi, Anurag Dwarakanath, Alex Kass, and Mani Suma Kuriakose. 2016. Dynamics of software development crowdsourcing. Proceedings - 11th IEEE International Conference on Global Software Engineering, ICGSE 2016(2016), 49–58. cited By 17.
Jeff Howe et al. 2006. The rise of crowdsourcing. Wired magazine 14, 6 (2006), 1–4.
Ann Joseph, Reshma Sebastian, and M. Soumya Krishnan. 2019. Crowd sourced software engineering; an agile approach. International Journal of Recent Technology and Engineering 8, 1(2019), 825–831. cited By 0.
Thomas D. LaToza and André van der Hoek. 2016. Crowdsourcing in software engineering: Models, motivations, and challenges. IEEE Software 33, 1 (2016), 74–80. https://doi.org/10.1109/MS.2016.12 cited By 99.
Leticia Machado, Josiane Kroll, Sabrina Marczak, and Rafael Prikladnicki. 2016. Breaking collaboration barriers through communication practices in software crowdsourcing. Proceedings - 11th IEEE International Conference on Global Software Engineering, ICGSE 2016(2016), 44–48. cited By 10.
mturk.com 2022. Amazon Mechanical Turk. Retrieved January 19, 2022 from https://www.mturk.com/
Madhukar Pai, Michael McCulloch, Jennifer D Gorman, Nitika Pai, Wayne Enanoria, Gail Kennedy, Prathap Tharyan, and John M Colford Jr. 2004. Systematic reviews and meta-analyses: An illustrated step-by-step guide. The National medical journal of India 17, 2 (2004), 86–95.
Himanshu Pandey. 2018. Comparison of usage of crowdsourcing in traditional and agile software development methodologies on the basis of effectiveness. Reliability: Theory and Applications 13, 3 (2018), 32–44. cited By 2.
Razieh Lotfalian Saremi and Ye Yang. 2016. Empirical analysis on parallel tasks in crowdsourcing software development. Proceedings - 2015 30th IEEE/ACM International Conference on Automated Software Engineering Workshops, ASEW 2015 (2016), 28–34. https://doi.org/10.1109/ASEW.2015.22 cited By 7.
Razieh L. Saremi, Ye Yang, Guenther Ruhe, and David Messinger. 2017. Leveraging crowdsourcing for team elasticity: An empirical evaluation at topcoder. Proceedings - 2017 IEEE/ACM 39th International Conference on Software Engineering: Software Engineering in Practice Track, ICSE-SEIP 2017 (2017), 103–112. https://doi.org/10.1109/ICSE-SEIP.2017.2
Benjamin Satzger, Rostyslav Zabolotnyi, Schahram Dustdar, Stefan Wild, Martin Gaedke, Steffen Göbel, and Tobias Nestler. 2014. Toward Collaborative Software Engineering Leveraging the Crowd. 159–182 pages. cited By 6.
Thiago A. M. D. Silva. 2020. Fatores Motivacionais Para a Participação de Não Especialistas em Desenvolvimento de Sistemas de Informação em Iniciativas de Desenvolvimento Aberto e Colaborativo. Master's thesis. UNIRIO, Rio de Janeiro, Brazil.
Sean Siqueira, Ig Bittencourt, Seiji Isotani, and Bernardo Pereira Nunes. 2017. Information systems based on (linked) open data: From openness to innovation. Boscarioli, C.; Araujo, R. M.; Maciel, R. S. P. “I GranDSI-BR – Grand Research Challenges in Information Systems in Brazil 2016-2026”. Special Committee on Information Systems (CE-SI). Brazilian Computer Society (SBC). ISBN: [978-85-7669-384-0]. 2017. 184p.(2017). https://doi.org/10.5753/sbc.2884.0
start 2022. StArt - State of the Art through Systematic Review. Retrieved January 19, 2022 from http://lapes.dc.ufscar.br/tools/start
Klaas-Jan Stol and Brian Fitzgerald. 2014. Two's company, three's a crowd: A case study of crowdsourcing software development. Proceedings - International Conference on Software Engineering1 (2014), 187–198. cited By 127.
Wei Tek Tsai, Wenjun Wu, and Michael N. Huhns. 2014. Cloud-based software crowdsourcing. IEEE Internet Computing 18, 3 (2014), 78–83. cited By 37.
Xiaojing Yin, Jiwei Huang, Wei He, Wei Guo, Han Yu, and Lizhen Cui. 2021. Group task allocation approach for heterogeneous software crowdsourcing tasks. Peer-to-Peer Networking and Applications 14, 3 (2021), 1736–1747. cited By 0.
