  OpenAI's Codex, a GPT-3 like model trained on a large code corpus, has made
headlines in and outside of academia. Given a short user-provided description,
it is capable of synthesizing code snippets that are syntactically and
semantically valid in most cases. In this work, we want to investigate whether
Codex is able to localize and fix bugs, a task of central interest in the field
of automated program repair. Our initial evaluation uses the multi-language
QuixBugs benchmark (40 bugs in both Python and Java). We find that, despite not
being trained for APR, Codex is surprisingly effective, and competitive with
recent state of the art techniques. Our results also show that Codex is
slightly more successful at repairing Python than Java.
