| Title | Open Source Software Computed Risk Framework |
| Publication Type | Conference Paper |
| Year of Publication | 2022 |
| Authors | Chapman, Jon, Venugopalan, Hari |
| Conference Name | 2022 IEEE 17th International Conference on Computer Sciences and Information Technologies (CSIT) |
| Keywords | Big Data, big data security metrics, codes, computer security, Correlation, Data analysis, Ecosystems, Force, information technology, Measurement, Prediction methods, pubcrawl, resilience, Resiliency, Scalability |
| Abstract | The increased dissemination of open source software to a broader audience has led to a proportional increase in the dissemination of vulnerabilities. These vulnerabilities are introduced by developers, some intentionally or negligently. In this paper, we work to quantity the relative risk that a given developer represents to a software project. We propose using empirical software engineering based analysis on the vast data made available by GitHub to create a Developer Risk Score (DRS) for prolific contributors on GitHub. The DRS can then be aggregated across a project as a derived vulnerability assessment, we call this the Computational Vulnerability Assessment Score (CVAS). The CVAS represents the correlation between the Developer Risk score across projects and vulnerabilities attributed to those projects. We believe this to be a contribution in trying to quantity risk introduced by specific developers across open source projects. Both of the risk scores, those for contributors and projects, are derived from an amalgamation of data, both from GitHub and outside GitHub. We seek to provide this risk metric as a force multiplier for the project maintainers that are responsible for reviewing code contributions. We hope this will lead to a reduction in the number of introduced vulnerabilities for projects in the Open Source ecosystem. |
| Notes | ISSN: 2766-3639 |
| DOI | 10.1109/CSIT56902.2022.10000561 |
| Citation Key | chapman_open_2022 |
Open Source Software Computed Risk Framework