The IRMA Community
Newsletters
Research IRM
Click a keyword to search titles using our InfoSci-OnDemand powered search:
|
Task Assignment and Personality: Crowdsourcing Software Development
|
Author(s): Abdul Rehman Gilal (Sukkur IBA University, Pakistan), Muhammad Zahid Tunio (Beijing University of Posts and Telecommunication, China), Ahmad Waqas (Sukkur IBA University, Pakistan), Malek Ahmad Almomani (University Technology PETRONAS, Malaysia), Sajid Khan (Sukkur IBA University, Pakistan)and Ruqaya Gilal (Universiti Utara Malaysia, Malaysia)
Copyright: 2022
Pages: 15
Source title:
Research Anthology on Agile Software, Software Development, and Testing
Source Author(s)/Editor(s): Information Resources Management Association (USA)
DOI: 10.4018/978-1-6684-3702-5.ch086
Purchase
|
Abstract
An open call format of crowdsourcing software development (CSD) is harnessing potential, diverse, and unlimited people. But, several thousand solutions are being submitted at platform against each call. To select and match the submitted task with the appropriate worker and vice versa is still a complicated problem. Focusing the issue, this study proposes a task assignment algorithm (TAA) that will behave as an intermediate facilitator (at platform) between task (from requester) and solution (from worker). The algorithm will divide the tasks' list based on the developer's personality. In this way, we can save the time of both developers and platform by reducing the searching time.
Related Content
Subhadip Kowar, Sneha Mukherjee, Shramana Ghosh.
© 2025.
26 pages.
|
C. V. Suresh Babu, Mala Raja Sekhar, A. Sachin, Bala Brindha.
© 2025.
26 pages.
|
A. D. N. Sarma.
© 2025.
32 pages.
|
Muhammad Usman Tariq.
© 2025.
26 pages.
|
Maaike Stoops, Pablo Alfonso Aguilar Calderón, Óscar Manuel Peña Bañuelos.
© 2025.
30 pages.
|
Pablo Alfonso Aguilar Calderón, José Alfonso Aguilar-Calderón, Dominik Morales-Silva, Carolina Tripp-Barba, Pedro Alfonso Aguilar-Calderón, Aníbal Zaldívar-Colado, Oscar Manuel Peña-Bañuelos.
© 2025.
30 pages.
|
Carlos Villarrubia, David Granada, Juan Manuel Vara.
© 2025.
34 pages.
|
|
|