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Large open-source projects such as the Linux kernel provide a unique opportunity to analyze many of the socio-technical processes of open-source software development. Understanding how cognitive workload affects the quality of code and productivity of work in such environments can help better protect open-source projects from potential vulnerabilities and better utilize limited developer resources.

In this paper, we present two agent-based simulation models of developer interactions on the Linux Kernel Mailing List (LKML). We also develop several non-simulation machine learning (ML) models predicting patch reversal, to compare with our agent-based simulation models. In our experiments, simulation models perform slightly better than ML models at predicting the expected number and proportion of reverted patches, and considerably better in matching the distribution of these values. Results are further …