Automatically Benchmarking LLM Code Agents through Agent-Driven Annotation and Evaluation
PositiveArtificial Intelligence
Recent advancements in code agents are revolutionizing automated software development, thanks to large language models (LLMs) and popular tools. However, current benchmarks for evaluating these code agents are hindered by high costs and the need for specialized knowledge, along with inflexible metrics that mainly depend on unit tests. This article introduces a novel agent-driven benchmark construction pipeline that aims to overcome these obstacles, making it easier to assess the performance of code agents. This innovation is significant as it could streamline the evaluation process and enhance the effectiveness of automated software development.
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