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

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.