Trust in Motion: Capturing Trust Ascendancy in Open-Source Projects using Hybrid AI

TL;DR

This paper introduces a dynamic methodology to understand and track trust development among contributors in open-source projects, emphasizing the importance of trust ascendancy in influencing project collaboration.

Contribution

It presents a novel approach to localize and analyze trust ascendancy signals dynamically, moving beyond static trust measurement methods in open-source development.

Findings

Effective in capturing trust ascendancy during a social engineering attack

Demonstrates the importance of dynamic trust analysis in open-source collaboration

Highlights future research directions for automated trust modeling

Abstract

Open-source is frequently described as a driver for unprecedented communication and collaboration, and the process works best when projects support teamwork. Yet, open-source cooperation processes in no way protect project contributors from considerations of trust, power, and influence. Indeed, achieving the level of trust necessary to contribute to a project and thus influence its direction is a constant process of change, and developers take many different routes over many communication channels to achieve it. We refer to this process of influence-seeking and trust-building as trust ascendancy. This paper describes a methodology for understanding the notion of trust ascendancy and introduces the capabilities that are needed to localize trust ascendancy operations happening over open-source projects. Much of the prior work in understanding trust in open-source software development has focused on a static view of the problem using different forms of quantity measures. However, trust ascendancy is not static, but rather adapts to changes in the open-source ecosystem in response to new input. This paper is the first attempt to articulate and study these signals from a dynamic view of the problem. In that respect, we identify related work that may help illuminate research challenges, implementation tradeoffs, and complementary solutions. Our preliminary results show the effectiveness of our method at capturing the trust ascendancy developed by individuals involved in a well-documented 2020 social engineering attack. Our future plans highlight research challenges and encourage cross-disciplinary collaboration to create more automated, accurate, and efficient ways to model and then track trust ascendancy in open-source projects.