EvoCodeBench: A Comprehensive Benchmark for Evaluating Large Language Models in Real-World Code Generation

Core Concepts

EvoCodeBench is a new code generation benchmark that aligns with real-world code repositories in multiple dimensions, including code distributions, dependency distributions, and comprehensive annotations. It proposes a repository-level code generation task to simulate the practical coding process and evaluates the coding abilities of 10 popular Large Language Models.

Abstract

EvoCodeBench is a new code generation benchmark that addresses the limitations of existing benchmarks. It has the following key features:

Alignment with Real-world Repositories: EvoCodeBench is collected from high-quality open-source repositories, ensuring that the code distribution and dependency distribution are consistent with real-world repositories.

Comprehensive Annotations: EvoCodeBench provides detailed requirements, reference code, reference dependencies, and the complete repository for each sample, enabling comprehensive evaluation of code generation abilities.

Robust Evaluation Metrics: EvoCodeBench uses Pass@k to assess functional correctness and Recall@k to evaluate the generation of relevant dependencies.

Evolving Benchmark: EvoCodeBench is designed as an evolving benchmark, with new versions released periodically to avoid data leakage.

Based on EvoCodeBench, the paper proposes a repository-level code generation task, which simulates the practical coding process. The authors evaluate 10 popular Large Language Models, including gpt-4, gpt-3.5, DeepSeek Coder, StarCoder 2, CodeLLaMa, Gemma, and Qwen 1.5, on this task. The results reveal that the coding abilities of these models are much lower in real-world repositories compared to their performance on previous benchmarks, highlighting the importance of evaluating code generation in realistic scenarios.
The paper also analyzes the failed cases and summarizes the shortcomings of existing Large Language Models in EvoCodeBench, providing insights for future improvements.

Stats

The average number of dependencies per program in EvoCodeBench-2403 is 3.46.
The average number of dependencies per program in 500 real-world repositories is 3.22.

Quotes

"EvoCodeBench aligns with real-world repositories in multiple dimensions, e.g., code distributions and dependency distributions."
"EvoCodeBench offers comprehensive annotations (e.g., requirements, reference code, and reference dependencies), and robust evaluation metrics (e.g., Pass@k and Recall@k)."
"EvoCodeBench is an evolving benchmark to avoid data leakage."

Key Insights Distilled From

EvoCodeBench

by Jia Li,Ge Li... at arxiv.org 04-02-2024

https://arxiv.org/pdf/2404.00599.pdf

Deeper Inquiries

How can the performance of Large Language Models on EvoCodeBench be further improved by leveraging more advanced techniques, such as retrieval-augmented generation or tool-augmented generation?

To enhance the performance of Large Language Models (LLMs) on EvoCodeBench, leveraging advanced techniques like retrieval-augmented generation and tool-augmented generation can be beneficial.

Retrieval-Augmented Generation (RAG):

Context Enrichment: RAG can help LLMs access additional information from a retrieval corpus, such as similar functions or code snippets, to provide more context for generating code. This can improve the understanding of dependencies and logic within the code.
Improved Code Generation: By incorporating relevant information retrieved from similar functions, LLMs can generate more accurate and contextually relevant code. This approach can help address the limitations of solely relying on the provided requirements and code snippets.

Tool-Augmented Generation:

Integration of Development Tools: Integrate development tools or plugins that provide real-time feedback and suggestions to LLMs during code generation. These tools can assist in identifying errors, suggesting improvements, and enhancing the overall coding process.
Interactive Code Generation: Implement interactive interfaces where LLMs can interact with code editors or development environments to refine their generated code based on immediate feedback. This interactive approach can lead to iterative improvements in code quality.

By incorporating these advanced techniques, LLMs can benefit from additional context, feedback, and tools to enhance their code generation capabilities on EvoCodeBench.

What are the potential challenges and limitations of using auto-generated requirements in EvoCodeBench, and how can they be addressed to improve the benchmark's quality?

Using auto-generated requirements in EvoCodeBench can introduce certain challenges and limitations that need to be addressed to enhance the benchmark's quality:

Completeness and Clarity:

Challenge: Auto-generated requirements may lack completeness or clarity compared to human-written requirements, potentially leading to ambiguity or missing details.
Addressing: Implement validation mechanisms to ensure that auto-generated requirements cover all essential aspects of the code functionality. Fine-tune the language model prompts to prioritize clarity and specificity in requirement generation.

Domain-Specific Knowledge:

Challenge: Auto-generated requirements may lack domain-specific knowledge or context, impacting the accuracy and relevance of the generated code.
Addressing: Incorporate domain-specific prompts or training data to enhance the language model's understanding of specific coding domains. Introduce specialized prompts for different types of code generation tasks to improve the relevance of requirements.

Consistency and Accuracy:

Challenge: Ensuring consistency and accuracy in auto-generated requirements across different samples can be challenging, leading to variations in quality.
Addressing: Implement quality control measures to validate the coherence and accuracy of auto-generated requirements. Use human annotators to review and refine requirements where necessary to maintain consistency and quality standards.

By addressing these challenges through validation mechanisms, domain-specific training, and quality control measures, the use of auto-generated requirements in EvoCodeBench can be optimized to improve benchmark quality and reliability.

Given the monolingual nature of EvoCodeBench, how can the benchmark be extended to support multiple natural languages and programming languages, and what are the potential benefits and challenges of such an extension?

Extending EvoCodeBench to support multiple natural languages and programming languages can offer several benefits but also pose challenges that need to be carefully addressed:

Benefits:

Language Diversity: Supporting multiple natural languages enables a more inclusive evaluation of LLMs across different linguistic contexts, catering to a global audience of developers.
Programming Language Flexibility: Extending support to various programming languages allows for a broader assessment of LLMs' code generation capabilities, reflecting real-world coding scenarios more accurately.
Cross-Linguistic Analysis: Comparative analysis across languages can provide insights into the language-specific strengths and weaknesses of LLMs, facilitating targeted improvements.

Challenges:

Data Collection and Annotation: Gathering diverse datasets in multiple languages and ensuring accurate annotations for code samples and requirements can be resource-intensive and time-consuming.
Model Adaptation: Adapting LLMs to effectively generate code in different languages and programming paradigms requires extensive fine-tuning and training on multilingual and multi-domain data.
Evaluation Consistency: Ensuring consistent evaluation metrics and benchmarks across languages and programming languages is crucial for fair comparisons but may pose challenges due to linguistic and syntactic variations.

To address these challenges and leverage the benefits of multilingual and multi-language support in EvoCodeBench, a systematic approach involving robust data collection, model adaptation strategies, and standardized evaluation protocols will be essential. Collaborations with linguists, domain experts, and developers from diverse language backgrounds can also enrich the benchmark and enhance its relevance in a global context.