CodeBenchGen: A Scalable Framework for Creating Execution-based Code Generation Benchmarks

Core Concepts

CodeBenchGen is a framework that leverages large language models to automatically convert arbitrary code fragments into executable evaluation examples, enabling the creation of custom and scalable code generation benchmarks.

Abstract

The paper presents CodeBenchGen, a framework for creating execution-based code generation benchmarks. The key steps are:

Sandboxing: An LLM is used to sandbox the input code fragment, removing dependencies and creating an isolated execution environment.
Test Generation: The LLM is used to generate test cases to verify the functionality of the generated code.
Iterative Execution and Debugging: The generated code and tests are iteratively executed and debugged using the LLM until the target code can pass all test cases.
Post-processing: Natural language instructions are generated, additional tests are added, and a shared runtime environment is set up.

The authors demonstrate the scalability of CodeBenchGen by creating a new benchmark, Exec-CSN, using 4,079 code fragments from the CodeSearchNet dataset. Exec-CSN contains 1,931 examples covering 293 libraries and 668 repository topics.
The authors conduct a corpus-based evaluation and a human study to verify the quality of Exec-CSN. The results show that Exec-CSN has high domain diversity, with examples of varying complexity that are generally solvable by humans. Code generation experiments on 10 models, including proprietary and open-source models, reveal the complexity of the dataset, with the best model achieving a Pass@1 score of only 37.21%.

Stats

The Exec-CSN dataset contains 1,931 examples.
The examples cover 293 libraries (118 standard, 175 external) and 668 repository topics.
The examples are taken from 367 GitHub repositories, with the number of contributors ranging from 1 to 449.

Quotes

"To facilitate evaluation of code generation systems across diverse scenarios, we present CodeBenchGen, a framework to create scalable execution-based benchmarks that only requires light guidance from humans."
"Results show that 81.3% of the examples are solvable by the participants. The problems have a range of difficulties: according to our survey questions, 39% of the examples can be 'quickly solved by most programmers,' 44% 'requires some effort to solve' and 17% 'can only be solved by professionals or with great effort.'"
"The best model (GPT-4-turbo) only achieves 37.21 Pass@1, indicating that there is still room for improvement on our dataset."

Key Insights Distilled From

CodeBenchGen

by Yiqing Xie,A... at arxiv.org 04-02-2024

https://arxiv.org/pdf/2404.00566.pdf

Deeper Inquiries

How can the CodeBenchGen framework be extended to support other programming languages beyond Python?

To extend the CodeBenchGen framework to support other programming languages, a few key steps can be taken:

Language Model Adaptation: Utilize language models trained on different programming languages to generate code examples in those languages. By fine-tuning or adapting existing models to understand the syntax and semantics of the target language, the framework can be made language-agnostic.

Language-specific Templates: Develop templates or rules specific to each programming language to guide the generation process. These templates can provide the necessary structure and constraints for generating code in a particular language.

Language-specific Test Cases: Create a repository of test cases tailored to different programming languages. These test cases can be used to evaluate the correctness of the generated code in languages other than Python.

Language-specific Execution Environments: Set up execution environments for each target language to ensure that the generated code can be executed and tested effectively.

Community Contributions: Encourage contributions from the programming community to provide examples, templates, and test cases in various languages, making the framework more versatile and inclusive of different programming paradigms.

By incorporating these strategies, the CodeBenchGen framework can be extended to support a wide range of programming languages, enabling comprehensive evaluation of code generation systems across diverse language ecosystems.

What are the potential biases or limitations in the code fragments selected from the CodeSearchNet dataset, and how could they be addressed in future iterations of the benchmark?

Potential biases or limitations in the code fragments selected from the CodeSearchNet dataset include:

Domain Specificity: The dataset may be biased towards certain domains or topics, leading to a lack of diversity in the types of code examples available.

Quality Variability: Code quality and complexity may vary significantly across the dataset, affecting the difficulty and solvability of the generated examples.

Dependency on Existing Test Cases: The reliance on existing test cases in the dataset may limit the scope of evaluation and overlook scenarios where test cases are not readily available.

To address these biases and limitations in future iterations of the benchmark, the following steps can be taken:

Diverse Data Sources: Incorporate code fragments from a wider range of sources beyond CodeSearchNet to ensure diversity in domains, coding styles, and complexity levels.

Quality Control Mechanisms: Implement quality control measures to filter out low-quality or incomplete code fragments, ensuring that the generated examples are of high standard and represent real-world coding scenarios.

Manual Annotation: Introduce manual annotation processes to validate the correctness and relevance of the selected code fragments, reducing biases introduced by automated selection methods.

Synthetic Data Generation: Augment the dataset with synthetically generated code examples to cover a broader spectrum of scenarios and mitigate biases present in the original dataset.

By addressing these biases and limitations, future iterations of the benchmark can provide a more comprehensive and unbiased evaluation of code generation systems.

How could the CodeBenchGen framework be integrated with other code generation evaluation approaches, such as human-in-the-loop assessment or open-ended task completion, to provide a more comprehensive evaluation of code generation systems?

Integrating the CodeBenchGen framework with other evaluation approaches can enhance the assessment of code generation systems. Here's how it can be done:

Human-in-the-Loop Assessment: Incorporate human feedback at various stages of the benchmark generation process. Humans can validate the generated examples, provide insights on the quality of instructions, and offer subjective evaluations that complement automated metrics.

Open-ended Task Completion: Include open-ended coding tasks where models are required to generate code solutions without specific prompts. This can test the system's creativity, adaptability, and problem-solving capabilities in a more unconstrained setting.

Iterative Improvement: Allow models and humans to iteratively refine their outputs based on feedback from test cases and human evaluators. This iterative process can lead to continuous improvement and better alignment with the desired outcomes.

Qualitative Analysis: Integrate qualitative analysis methods to capture nuanced aspects of code generation, such as code readability, maintainability, and adherence to best practices. This can provide a holistic view of the system's performance beyond quantitative metrics.

Real-world Scenario Simulation: Design evaluation tasks that simulate real-world coding scenarios, such as collaborative coding, code reviews, or debugging sessions. This can assess the system's ability to handle complex, multi-faceted coding challenges.

By combining the CodeBenchGen framework with these approaches, a more comprehensive evaluation of code generation systems can be achieved, encompassing both automated metrics and human-centric assessments for a well-rounded evaluation.