  With the rapid development of Large Language Models (LLMs), a large number of
machine learning models have been developed to assist programming tasks
including the generation of program code from natural language input. However,
how to evaluate such LLMs for this task is still an open problem despite of the
great amount of research efforts that have been made and reported to evaluate
and compare them. This paper provides a critical review of the existing work on
the testing and evaluation of these tools with a focus on two key aspects: the
benchmarks and the metrics used in the evaluations. Based on the review,
further research directions are discussed.
