Greedy Scheduling: A Neural Network Method to Reduce Task Failure in Software Crowdsourcing

Jordan Urbaczek, Razieh Saremi, Mostaan Lotfalian Saremi, Julian Togelius

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    Abstract

    Highly dynamic and competitive crowdsourcing software development (CSD) marketplaces may experience task failure due to unforeseen reasons, such as increased competition over shared supplier resources, or uncertainty associated with a dynamic worker supply. Existing analysis reveals an average task failure ratio of 15.7% in software crowdsourcing markets.These lead to an increasing need for scheduling support for CSD managers to improve the efficiency and predictability of crowdsourcing processes and outcomes. To that end, this research proposes a task scheduling method based on neural networks, and develop a system that can predict and analyze task failure probability upon arrival. More specifically, the model uses a range of input variables, including the number of open tasks in the platform, the average task similarity between arriving tasks and open tasks, the winner’s monetary prize, and task duration, to predict the probability of task failure on the planned arrival date and two surplus days. This prediction will offer the recommended day associated with lowest task failure probability to post the task. The model on average provided 4% lower failure probability per project. The proposed model empowers crowdsourcing managers to explore potential crowdsourcing outcomes with respect to different task arrival strategies.

    Original languageEnglish (US)
    Title of host publicationICEIS 2021 - Proceedings of the 23rd International Conference on Enterprise Information Systems
    EditorsJoaquim Filipe, Michal Smialek, Alexander Brodsky, Slimane Hammoudi
    PublisherScience and Technology Publications, Lda
    Pages410-419
    Number of pages10
    ISBN (Electronic)9789897585098
    StatePublished - 2021
    Event23rd International Conference on Enterprise Information Systems, ICEIS 2021 - Virtual, Online
    Duration: Apr 26 2021Apr 28 2021

    Publication series

    NameInternational Conference on Enterprise Information Systems, ICEIS - Proceedings
    Volume1
    ISSN (Electronic)2184-4992

    Conference

    Conference23rd International Conference on Enterprise Information Systems, ICEIS 2021
    CityVirtual, Online
    Period4/26/214/28/21

    Keywords

    • Crowdsourcing
    • Neural Network
    • Task Failure
    • Task Scheduling
    • Task Similarity
    • TopCoder

    ASJC Scopus subject areas

    • Information Systems
    • Computer Science Applications
    • Information Systems and Management

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