StudentEval: A Benchmark of Student-Written Prompts for Large Language
  Models of Code
Hannah McLean Babe Sydney Nguyen Yangtian Zi Arjun Guha Molly Q Feldman Carolyn Jane Anderson
Code LLMs are being rapidly deployed and there is evidence that they can make
professional programmers more productive. Current benchmarks for code
generation measure whether models generate correct programs given an expert
prompt. In this paper, we present a new benchmark containing multiple prompts
per problem, written by a specific population of non-expert prompters:
beginning programmers. StudentEval contains 1,749 prompts for 48 problems,
written by 80 students who have only completed one semester of Python
programming. Our students wrote these prompts while working interactively with
a Code LLM, and we observed very mixed success rates. We use StudentEval to
evaluate 5 Code LLMs and find that StudentEval is a better discriminator of
model performance than existing benchmarks. We analyze the prompts and find
significant variation in students' prompting techniques. We also find that
nondeterministic LLM sampling could mislead students into thinking that their
prompts are more (or less) effective than they actually are, which has
implications for how to teach with Code LLMs.