  Large language models (LLMs) have significantly improved their ability to
perform tasks in the field of code generation. However, there is still a gap
between LLMs being capable coders and being top-tier software engineers. Based
on the observation that top-level software engineers often ask clarifying
questions to reduce ambiguity in both requirements and coding solutions, we
argue that the same should be applied to LLMs for code generation tasks.
  In this work, we conducted an empirical study on the benchmark and analysis
of the communication skills of LLMs for code generation. We define
communication skills of LLMs as ``being able to ask clarifying questions when
the description of the code generation problem has issues''. We created a new
benchmark, HumanEvalComm, by modifying problem descriptions according to three
issues: inconsistency, ambiguity, incompleteness. We defined new evaluation
metrics such as Communication Rate and Good Question Rate, and then
experimented on HumanEvalComm with different Code LLMs, and a new LLM agent
approach, Okanagan, to identify and ask questions in ambiguous parts from code
and descriptions for further refining the generated code. Finally, we discussed
evaluation results by comparing Code LLMs and Okanagan with our findings.
