Copilot Evaluation Harness: Evaluating LLM-Guided Software Programming (2402.14261v1)

Abstract: The integration of LLMs into Development Environments (IDEs) has become a focal point in modern software development. LLMs such as OpenAI GPT-3.5/4 and Code Llama offer the potential to significantly augment developer productivity by serving as intelligent, chat-driven programming assistants. However, utilizing LLMs out of the box is unlikely to be optimal for any given scenario. Rather, each system requires the LLM to be honed to its set of heuristics to ensure the best performance. In this paper, we introduce the Copilot evaluation harness: a set of data and tools for evaluating LLM-guided IDE interactions, covering various programming scenarios and languages. We propose our metrics as a more robust and information-dense evaluation than previous state of the art evaluation systems. We design and compute both static and execution based success metrics for scenarios encompassing a wide range of developer tasks, including code generation from natural language (generate), documentation generation from code (doc), test case generation (test), bug-fixing (fix), and workspace understanding and query resolution (workspace). These success metrics are designed to evaluate the performance of LLMs within a given IDE and its respective parameter space. Our learnings from evaluating three common LLMs using these metrics can inform the development and validation of future scenarios in LLM guided IDEs.

• The paper introduces the Copilot evaluation harness, a comprehensive framework for evaluating LLM-guided programming across various IDE tasks.
• It employs both static and execution-based metrics to rigorously assess documentation generation, bug-fixing, code synthesis, and test case generation.
• Comparative analysis reveals GPT-4’s superior performance in documentation tasks while highlighting challenges in complex bug-fixing scenarios.

Copilot Evaluation Harness: Evaluating LLM-Guided Software Programming

Introduction

The paper proposes an evaluation framework for assessing the integration of LLMs within Integrated Development Environments (IDEs), specifically focusing on models such as GPT-3.5, GPT-4, and Code Llama. These models have the potential to augment developer productivity by serving as intelligent coding assistants. The paper introduces the Copilot evaluation harness—a set of tools and metrics designed to evaluate LLM-guided programming across various developer tasks, including code generation, documentation, bug-fixing, test case generation, and workspace comprehension.

Methodology

The Copilot evaluation harness provides both static and execution-based success metrics, enabling a comprehensive assessment of LLM performance in software development scenarios.

1. Documentation Generation: Metrics like syntax correctness and format correctness are employed to evaluate the quality of automatically generated documentation.
2. Bug-Fixing: The system assesses the ability of models to resolve static analysis warnings by employing tools like ESLint for JavaScript and TypeScript, Pylint for Python, etc. Success is measured by the syntax correctness of the fixed code and the resolution of initial errors without introducing new ones.
3. Code Generation: The system focuses on the ability to convert natural language descriptions into syntactically and functionally correct code snippets, validated through test pass rates.
4. Test Generation: LLMs generate tests based on provided method signatures and bodies. Success is measured by the syntax correctness of the generated tests and their execution results.
5. Workspace Understanding: The model's retrieval and comprehension capabilities are evaluated using metrics like Mean Reciprocal Rank (MRR).

Implementation Considerations

The evaluation harness was applied to over 700,000 IDE instances across various programming languages, building a comprehensive dataset for testing LLM performance in documentation and bug-fixing tasks.

• Data Collection: Method selection involves ensuring code files are syntactically complete and utilize popular frameworks (e.g., npm for JavaScript).
• Test Case Generation: The system generates evaluation-specific test cases, such as methods documented or affected by static analysis errors.
• Model Comparison: The paper reports comparative results for the tested models, revealing insights into LLMs' strengths and weaknesses across different programming tasks.

Results

The paper highlights differences in performance between GPT-4, GPT-3.5, and Code Llama:

• GPT-4: Generally outperforms GPT-3.5 and Code Llama in documentation tasks but struggles with more complex bug-fixing scenarios due to its nuanced approach, which can introduce new issues.
• GPT-3.5: Achieves high syntax completeness, occasionally succeeding by opting for rudimentary solutions, as observed in bug-fixing tasks.
• Code Llama: Lags behind the GPT models, especially in tasks requiring comprehension across a wide code and context spectrum.

Insights and Improvements

The framework provides actionable insights for improving LLM integration:

• Enhancing prompts for accuracy and reducing over-fitted solutions.
• Providing precise instructions within IDE integrations, leading to improved model compliance with instructions.

Conclusion and Future Work

The Copilot evaluation harness establishes a robust framework for the evaluation of LLM-integrated IDEs, offering a systematic way to optimize these tools for real-world programming tasks. It highlights the necessity for detailed evaluation to fine-tune LLM parameters for superior integration results. Future work involves extending the harness for more comprehensive metric coverage and open-sourcing both the data and evaluation code for broader community usage.