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The use of third-party libraries to manage software complexity can expose open source software projects to vulnerabilities. However, project owners do not currently have a standard way to enable private disclosure of potential security vulnerabilities. This neglect may be caused in part by having no template to follow for disclosing such vulnerabilities. We analyzed 600 GitHub projects to determine how many projects contained a vulnerable dependency and whether the projects had a process in place to privately communicate security issues. We found that 385 out of 600 open source Java projects contained at least one vulnerable dependency, and only 13 of those 385 projects had a security vulnerability reporting process. That is, 96.6% of the projects with a vulnerability did not have a security notification process in place to allow for private disclosure. In determining whether the projects even had contact information publicly available, we found that 19.8% had no contact information publicly available, let alone a security vulnerability reporting process. We suggest two methods to allow for community members to privately disclose potential security vulnerabilities. 

The computational modeling of groups requires models that connect micro-level with macro-level processes and outcomes. Recent research in computational social science has started from simple models of human behavior, and attempted to link to social structures. However, these models make simplifying assumptions about human understanding of culture that are often not realistic and may be limiting in their generality. In this paper, we present work on Bayesian affect control theory as a more comprehensive, yet highly parsimonious model that integrates artificial intelligence, social psychology, and emotions into a single predictive model of human activities in groups. We illustrate these developments with examples from an ongoing research project aimed at computational analysis of virtual software development teams.