  Large Language Models (LLMs) are gaining popularity among software engineers.
A crucial aspect of developing effective code-generation LLMs is to evaluate
these models using a robust benchmark. Evaluation benchmarks with quality
issues can provide a false sense of performance. In this work, we conduct the
first-of-its-kind study of the quality of prompts within benchmarks used to
compare the performance of different code generation models. To conduct this
study, we analyzed 3,566 prompts from 9 code generation benchmarks to identify
quality issues in them. We also investigated whether fixing the identified
quality issues in the benchmarks' prompts affects a model's performance. We
also studied memorization issues of the evaluation dataset, which can put into
question a benchmark's trustworthiness. We found that code generation
evaluation benchmarks mainly focused on Python and coding exercises and had
very limited contextual dependencies to challenge the model. These datasets and
the developers' prompts suffer from quality issues like spelling and
grammatical errors, unclear sentences to express developers' intent, and not
using proper documentation style. Fixing all these issues in the benchmarks can
lead to a better performance for Python code generation, but not a significant
improvement was observed for Java code generation. We also found evidence that
GPT-3.5-Turbo and CodeGen-2.5 models possibly have data contamination issues.
