  A typical crowdsourcing software development(CSD) marketplace consists of a
list of software tasks as service demands and a pool of freelancer developers
as service suppliers. Highly dynamic and competitive CSD market places may
result in task failure due to unforeseen risks, such as increased competition
over shared worker supply, or uncertainty associated with workers' experience
and skills, and so on. To improve CSD effectiveness, it is essential to better
understand and plan with respect to dynamic worker characteristics and risks
associated with CSD processes. In this paper, we present a hybrid simulation
model, CrowdSim, to forecast crowdsourcing task failure risk in competitive CSD
platforms. CrowdSim is composed of three layered components: the macro-level
reflects the overall crowdsourcing platform based on system dynamics,the
meso-level represents the task life cycle based on discrete event simulation,
and the micro-level models the crowd workers' decision-making processes based
on agent-based simulation. CrowdSim is evaluated through three CSD decision
scenarios to demonstrate its effectiveness, using a real-world historical
dataset and the results demonstrate CrowdSim's potential in empowering
crowdsourcing managers to explore crowdsourcing outcomes with respect to
different task scheduling options.
