A Data-Driven Game Theoretic Strategy for Developers in Software Crowdsourcing: A Case Study

Zhifang Liao,Zhi Zeng,Xiaoping Fan,Yan Zhang

doi:10.3390/app9040721

Abstract

Crowdsourcing has the advantages of being cost-effective and saving time, which is a typical embodiment of collective wisdom and community workers’ collaborative development. However, this development paradigm of software crowdsourcing has not been used widely. A very important reason is that requesters have limited knowledge about crowd workers’ professional skills and qualities. Another reason is that the crowd workers in the competition cannot get the appropriate reward, which affects their motivation. To solve this problem, this paper proposes a method of maximizing reward based on the crowdsourcing ability of workers, they can choose tasks according to their own abilities to obtain appropriate bonuses. Our method includes two steps: Firstly, it puts forward a method to evaluate the crowd workers’ ability, then it analyzes the intensity of competition for tasks at Topcoder.com—an open community crowdsourcing platform—on the basis of the workers’ crowdsourcing ability; secondly, it follows dynamic programming ideas and builds game models under complete information in different cases, offering a strategy of reward maximization for workers by solving a mixed-strategy Nash equilibrium. This paper employs crowdsourcing data from Topcoder.com to carry out experiments. The experimental results show that the distribution of workers’ crowdsourcing ability is uneven, and to some extent it can show the activity degree of crowdsourcing tasks. Meanwhile, according to the strategy of reward maximization, a crowd worker can get the theoretically maximum reward.

Highlights

Crowdsourcing is used to find high-quality workers in the masses through the Internet; workers can get paid when they complete tasks
The process of crowdsourcing software development is to find the task distributed by the platform, register the task, submit programs, review of programs, publish the notice, and collect the reward
Two datasets from Topcoder.com were mainly used in this paper: One was the personal information of the crowd workers and the other was the information of the crowdsourcing tasks

Summary

Introduction

Crowdsourcing is used to find high-quality workers in the masses through the Internet; workers can get paid when they complete tasks. For data mining and knowledge acquisition [3]; it can be used to select high-quality workers from a large number of people at a small cost. Such services from people of the group only cost a small amount of money. Software development is rarely done in an isolated environment [4,5]; instead, it increasingly depends on the collaboration among different groups of stakeholders Such cooperative development processes frequently face challenges from rapidly evolving requirements, conflicting stakeholders needs and constraints, as well as other factors. Guzman E et al [19] used the software and demand evolution information contained in tweets, and presented a survey, with 84 software engineering practitioners and researchers, that studies the tweet attributes that are most telling of tweet priority when performing software evolution tasks

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