Trustworthiness in Enterprise Crowdsourcing: a Taxonomy & evidence from data

TL;DR

This paper investigates trustworthiness in enterprise crowdsourcing for software development by analyzing risks, mitigation techniques, and empirical data to provide guidelines for improving trust in such environments.

Contribution

It offers a comprehensive taxonomy of trust risks, reviews mitigation strategies, and presents empirical evidence from real-world data to evaluate trustworthiness metrics.

Findings

Identification of key trust risks in enterprise crowdsourcing

Evaluation of effectiveness of mitigation techniques

Empirical data on untrustworthy behavior and mitigation performance

Abstract

In this paper we study the trustworthiness of the crowd for crowdsourced software development. Through the study of literature from various domains, we present the risks that impact the trustworthiness in an enterprise context. We survey known techniques to mitigate these risks. We also analyze key metrics from multiple years of empirical data of actual crowdsourced software development tasks from two leading vendors. We present the metrics around untrustworthy behavior and the performance of certain mitigation techniques. Our study and results can serve as guidelines for crowdsourced enterprise software development.