  Context: 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.
  Goal: The objective of this study is to provide a task scheduling
recommendation model for software crowdsourcing platforms in order to improve
the success and efficiency of software crowdsourcing.
  Method: We propose 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 the lowest task failure probability to post the task. The
proposed model is based on the workflow and data of Topcoder, one of the
primary software crowdsourcing platforms.
  Results: We present a model that suggests the best recommended arrival dates
for any task in the project with surplus of two days per task in the project.
The model on average provided 4\% lower failure ratio per project.
