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The Codex model has demonstrated extraordinary competence in synthesizingcode from natural language problem descriptions. However, in order to revealunknown failure modes and hidden biases, such large-scale models must besystematically subjected to multiple and diverse evaluation studies.  In this work, we evaluate the code synthesis capabilities of the Codex modelbased on a set of 115 Python problem statements from a popular competitiveprogramming portal: HackerRank. Our evaluation shows that Codex is indeedproficient in Python, solving 96% of the problems in a zero-shot setting, and100% of the problems in a few-shot setting. However, Codex exhibits clear signsof generating memorized code based on our evaluation. This is alarming,especially since the adoption and use of such models could directly impact howcode is written and produced in the foreseeable future. With this in mind, wefurther discuss and highlight some of the prominent risks associated withlarge-scale models of source code. Finally, we propose a framework forcode-synthesis evaluation using variations of problem statements based onmutations.

Codex Hacks HackerRank: Memorization Issues and a Framework for Code Synthesis Evaluation