Context: Highly dynamic and competitive crowd-sourcing 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 model based on neural networks, and develop an approach to predict and analyze task failure probability upon arrival. More specifically, the model uses number of open tasks in the platform, average task similarity level of new arrival task with open tasks, task monetary prize and task duration as input, and then predicts the probability of task failure on the planned arrival date with three surplus days and recommending the day associated with 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 three days per task in the project. The model on average provided 4\% lower failure ratio per project.Conclusions: The proposed model empowers crowdsourcing managers to explore potential crowdsourcing outcomes with respect to different task arrival strategies. \\%DIF > 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.
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 \\%DIF > 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.
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crowd-sourcing
crowdsourcing
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\% 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 model based on neural networks, and develop an approach to predict and analyze task failure probability upon arrival. More specifically, the model uses number of open tasks in the platform, average task similarity level of new arrival task with open tasks, task monetary prize and task duration as input, and then predicts the probability of task failure on the planned arrival date with three surplus days and recommending the day associated with 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 three days per task in the project. The model on average provided 4\% lower failure ratio per project.Conclusions: The proposed model empowers crowdsourcing managers to explore potential crowdsourcing outcomes with respect to different task arrival strategies.