Comprehensive Evaluation of Large Language Models for Code Editing Tasks

Core Concepts

CodeEditorBench is a pioneering benchmark designed to rigorously assess the performance of Large Language Models (LLMs) in various code editing tasks, including debugging, translating, polishing, and requirement switching.

Abstract

The authors introduce CodeEditorBench, a comprehensive evaluation framework for assessing the code editing capabilities of Large Language Models (LLMs). The benchmark covers a diverse range of programming languages, complexity levels, and editing tasks, aiming to mirror real-world software development scenarios.
Key highlights:

The dataset is curated from five sources, including LeetCode, CodeContests, CodeXGLUE, CodeNet, and Taco, covering a wide spectrum of data structures, algorithms, and computational problems.
The benchmark evaluates LLMs across four scenarios: Code Debug, Code Translate, Code Polish, and Code Requirement Switch, each presenting unique challenges.
The authors employ various prompting techniques, including zero-shot and few-shot, to evaluate 19 popular LLMs, both open-source and closed-source, ranging in size from 6.7B to 34B parameters.
The evaluation results reveal that closed-source models, particularly Gemini-Ultra and GPT-4, outperform open-source models in CodeEditorBench, highlighting differences in model performance based on problem types and prompt sensitivities.
The analysis also identifies areas where smaller models surpass their larger counterparts in efficiency, and underscores the variability in model performance across different problem categories.
The authors aim to release all prompts and datasets to enable the community to expand the benchmark and evaluate emerging LLMs, contributing to the advancement of code editing capabilities.

Stats

"The dataset is curated from five sources, including LeetCode, CodeContests, CodeXGLUE, CodeNet, and Taco, covering a wide spectrum of data structures, algorithms, and computational problems."
"The benchmark evaluates LLMs across four scenarios: Code Debug, Code Translate, Code Polish, and Code Requirement Switch, each presenting unique challenges."
"The authors employ various prompting techniques, including zero-shot and few-shot, to evaluate 19 popular LLMs, both open-source and closed-source, ranging in size from 6.7B to 34B parameters."

Quotes

"CodeEditorBench is a pioneering evaluation framework designed to assess the performance of LLMs in editing code rigorously, where the overview is described in Figure 1."
"Closed-source models, particularly Gemini-Ultra and GPT-4, outperform open-source models in CodeEditorBench, highlighting differences in model performance based on problem types and prompt sensitivities."

Key Insights Distilled From

CodeEditorBench

by Jiawei Guo,Z... at arxiv.org 04-05-2024

https://arxiv.org/pdf/2404.03543.pdf

Deeper Inquiries

How can the CodeEditorBench dataset be further expanded to include a broader range of programming languages, problem types, and real-world software development scenarios?

Expanding the CodeEditorBench dataset to encompass a wider array of programming languages, problem types, and real-world software development scenarios can be achieved through several strategies:

Inclusion of Additional Programming Languages: To broaden the dataset's coverage, new programming languages should be incorporated. This can involve sourcing coding challenges from platforms that focus on languages not currently well-represented in the dataset.

Diversification of Problem Types: Introducing a more extensive range of problem types, such as data structures, algorithms, system design, and specific domain-related challenges, can enhance the dataset's complexity and applicability to diverse software development scenarios.

Real-World Scenario Simulation: To better reflect real-world software development, scenarios involving industry-specific challenges, collaborative coding tasks, version control systems, and integration with external APIs can be included. This will provide a more practical evaluation environment for LLMs.

Community Contribution: Encouraging contributions from the programming community to suggest new problem types, languages, and scenarios can help in expanding the dataset organically. Open-sourcing the dataset and inviting submissions can lead to a more comprehensive and diverse collection of coding challenges.

Continuous Iteration and Updates: Regularly updating the dataset with new challenges, feedback from users, and emerging trends in software development will ensure that the CodeEditorBench remains relevant and reflective of the evolving landscape of coding practices.

How can the potential biases or limitations in the current dataset selection and curation process be addressed to ensure a more comprehensive and unbiased evaluation?

Addressing potential biases and limitations in the dataset selection and curation process is crucial for ensuring a fair and unbiased evaluation of LLMs in code editing tasks:

Diverse Data Sources: To mitigate bias, data should be sourced from a wide range of platforms, coding contests, and repositories to ensure representation across different coding styles, difficulty levels, and problem domains.

Balanced Representation: Ensuring an equitable distribution of programming languages, problem types, and difficulty levels in the dataset can prevent over-representation of certain categories, leading to a more balanced evaluation.

Random Sampling: Implementing random sampling techniques when selecting challenges for the dataset can help reduce selection bias and ensure that each problem type and language has an equal chance of inclusion.

Expert Review: Involving domain experts in the curation process can help identify and rectify any biases or inaccuracies in the dataset. Expert validation of the challenges and test cases can enhance the quality and fairness of the evaluation.

Transparency and Documentation: Providing detailed documentation on the dataset creation process, including sources, selection criteria, and any potential biases, can increase transparency and allow for external scrutiny and validation.

How can the CodeEditorBench framework be integrated with other code-related benchmarks and tools to provide a holistic assessment of LLMs' capabilities in the software development lifecycle?

Integrating the CodeEditorBench framework with other code-related benchmarks and tools can offer a comprehensive assessment of LLMs' capabilities across the software development lifecycle:

Cross-Benchmark Evaluation: Collaborating with existing code-related benchmarks like HumanEval, CodeNet, and EditEval to create a unified evaluation platform can provide a holistic view of LLM performance in various coding tasks.

Tool Integration: Integrating CodeEditorBench with code analysis tools, version control systems, and debugging platforms can enable a seamless transition from code editing tasks to testing, debugging, and deployment phases, offering a more comprehensive evaluation of LLMs' utility in real-world scenarios.

Metrics Alignment: Harmonizing evaluation metrics and criteria across different benchmarks can facilitate a standardized assessment of LLMs' code editing capabilities, ensuring consistency and comparability in performance evaluations.

Feedback Loop: Establishing a feedback loop between CodeEditorBench and other benchmarks or tools can enable continuous improvement and refinement of LLMs based on real-world usage scenarios and user feedback, enhancing their effectiveness in the software development lifecycle.

Interdisciplinary Collaboration: Collaborating with experts in software engineering, machine learning, and natural language processing fields can provide valuable insights into the integration of LLMs in the software development lifecycle, leading to a more holistic and informed evaluation framework.