Towards more realistic evaluation of LLM-based code generation: an experimental study and beyond

To evaluate the code generation capabilities of Large Language Models (LLMs) in complex real-world software development scenarios, many evaluation approaches have been developed. They typically leverage contextual code from the latest version of a project to facilitate LLMs in accurately generating the desired function. However, such evaluation approaches fail to consider the dynamic evolution of software projects over time, which we refer to as evolving-ignored situation, leading to issues of future context leakage and useful context missing. This in turn results in inaccurate evaluation of LLMs' performance. In this paper, we conduct an empirical study to deeply understand LLMs' code generation performance within settings that reflect the evolving nature of software development. To achieve this, we first construct an evolving-aware repository-level code generation dataset, namely HumanEvo, equipped with an automated execution-based evaluation tool. Second, we manually categorize HumanEvo according to dependency levels to more comprehensively analyze the model's performance in generating functions with different dependency levels. Third, we conduct extensive experiments on HumanEvo with seven representative and diverse LLMs to verify the effectiveness of the proposed benchmark. We obtain many important findings through our experimental study. For example, we find that previous evolving-ignored evaluation approaches lead to inflated performance of the LLMs, ranging from 10.0% to 61.1%. Based on the findings, we give actionable suggestions on more realistic evaluation of LLMs on code generation. We also build a shared evolving-aware code generation toolbox to facilitate future research. Replication package including source code, datasets and appendix is available at https://github.com/DeepSoftwareAnalytics/EvoEval.