  Recent advancements in the field of natural language generation have
facilitated the use of large language models to assess the quality of generated
text. Although these models have shown promising results in tasks such as
machine translation and summarization, their applicability in code intelligence
tasks remains limited without human involvement. The complexity of programming
concepts required for such tasks makes it difficult to develop evaluation
metrics that align with human judgment. Token-matching-based metrics, such as
BLEU, have demonstrated weak correlations with human practitioners in code
intelligence tasks. Moreover, utilizing human-written test suites to evaluate
functional correctness can be challenging in domains with low resources. To
overcome these obstacles, we propose \texttt{ICE-Score}, a new evaluation
metric via instructing large language models (LLMs) for code assessments. Our
metric addresses the limitations of existing approaches by achieving superior
correlations with functional correctness and human preferences, without the
need for test oracles or references. We evaluate the efficacy of our metric on
two different aspects (\textit{human preference} and \textit{execution
success}) and four programming languages. Our results demonstrate that our
metric surpasses state-of-the-art metrics for code generation, delivering high
levels of accuracy and consistency across various programming languages and
tasks. We also make our evaluation metric and datasets available to the
public\footnote{\url{https://github.com/terryyz/ice-score}}, encouraging
further research in evaluating code intelligence tasks.
