We replicated the main result of DeepBugs, a bug detection algorithm for name-based bugs. The original authors evaluated it in three contexts: swapped-argument bugs, wrong binary operator,and wrong binary operator operands. We followed the algorithm and replicated the results for swapped-argument bugs. Our replication used independent implementations of the major components: training set generation, token vectorization, and neural network data pipeline, model, and loss function. Using the same dataset and the same testing process, we report comparable performance: within 2% of the accuracy reported by Pradel and Sen.