论文标题

信任运动:使用混合动力AI在开源项目中捕获信任的升级

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

论文作者

Sanchez, Huascar, Hitaj, Briland

论文摘要

开源通常被描述为前所未有的沟通和协作的驱动力,当项目支持团队合作时,该过程最有效。然而,开源合作过程绝不保护项目贡献者免受信任,权力和影响力的考虑。确实,达到为项目做出贡献所必需的信任水平,因此影响其方向是一个不断变化的过程,开发人员在许多沟通渠道上采取许多不同的路线来实现它。我们将这种寻求影响力和信任建设的过程称为信任的提升。 本文介绍了一种理解信任升级概念的方法,并介绍了在开源项目上本地化信任的上升效果所需的功能。理解开源软件开发信任的许多先前工作都集中在使用不同形式的数量度量的静态视图上。但是,信任的升级不是静态的,而是适应开源生态系统的变化,以响应新的输入。本文是从问题的动态视图中阐明和研究这些信号的首次尝试。在这方面,我们确定了可能有助于阐明研究挑战,实施权衡和补充解决方案的相关工作。我们的初步结果表明,我们方法在捕捉参与有据可查的2020年社会工程攻击的个人开发的信任的升级方面的有效性。我们的未来计划强调了研究挑战,并鼓励跨学科的合作,以创建更加自动化,准确和有效的方法来建模,然后在开源项目中跟踪信任的升级。

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.

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