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

TL;DR

This paper introduces a neural network-based task scheduling method for software crowdsourcing platforms that predicts task failure probabilities to optimize posting times, reducing failure rates and improving success.

Contribution

The study presents a novel neural network model for task scheduling in crowdsourcing, specifically tailored to software development markets, improving failure prediction accuracy.

Findings

The model predicts task failure probability with high accuracy.

Scheduling recommendations reduce task failure ratio by an average of 4%.

The approach is validated using data from Topcoder.

Abstract

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.