CoCo-Bench: A Comprehensive Code Benchmark For Multi-task Large Language Model Evaluation
This provides a more systematic evaluation framework for code-oriented LLMs, addressing a gap in existing benchmarks, though it is incremental as it builds on prior work.
The paper tackles the lack of comprehensive benchmarks for evaluating large language models in software engineering by introducing CoCo-Bench, which assesses models across four dimensions and reveals significant performance variations.
Large language models (LLMs) play a crucial role in software engineering, excelling in tasks like code generation and maintenance. However, existing benchmarks are often narrow in scope, focusing on a specific task and lack a comprehensive evaluation framework that reflects real-world applications. To address these gaps, we introduce CoCo-Bench (Comprehensive Code Benchmark), designed to evaluate LLMs across four critical dimensions: code understanding, code generation, code modification, and code review. These dimensions capture essential developer needs, ensuring a more systematic and representative evaluation. CoCo-Bench includes multiple programming languages and varying task difficulties, with rigorous manual review to ensure data quality and accuracy. Empirical results show that CoCo-Bench aligns with existing benchmarks while uncovering significant variations in model performance, effectively highlighting strengths and weaknesses. By offering a holistic and objective evaluation, CoCo-Bench provides valuable insights to guide future research and technological advancements in code-oriented LLMs, establishing a reliable benchmark for the field.