A Comprehensive Evaluation Study on Code Generation Capabilities of Large Language Models Under Prompt Variability

This study focuses on the impact of prompt variability on the code generation capabilities of large language models (LLMs). By constructing a composite evaluation framework, it systematically analyzes the performance differences of mainstream LLMs under different prompt conditions. The research reveals that prompt sensitivity is widespread, and there are significant differences in model robustness. It also proposes practical recommendations for developers, model designers, and evaluation systems, which have important guiding significance for the practical application of AI programming assistants.

The Importance of Prompt Engineering

Prompt engineering has become a core skill for using large language models. Well-designed prompts can yield high-quality outputs, while unrefined prompts may lead to incorrect results. This sensitivity is particularly pronounced in code generation tasks.

Limitations of Existing Evaluations

Most current code generation model evaluations use fixed prompt templates, ignoring the diversity of prompts in real scenarios, and thus fail to reflect the model's ability to respond reliably to different query styles.

Research Questions

• How sensitive are large language models to prompt variations?
• Are there differences in prompt robustness among different models?
• Which dimensions of prompts (e.g., level of detail, number of examples) have the greatest impact on generation quality?
• How to construct a more comprehensive evaluation framework to measure model prompt robustness?

Prompt Variant Generation Strategy

Define multiple dimensions of prompt variation:

• Level of Detail: Minimal/Standard/Detailed/Complete prompts
• Number of Examples: Zero-shot/Single-shot/Multi-shot
• Format Structure: Natural language/Structured template/Code comments/Conversational style
• Language Style: Formal technical language/Everyday spoken language/Pseudocode style

Evaluation Metric System

• Functional Correctness: Pass rate, boundary handling, logical completeness
• Code Quality: Readability, efficiency, adherence to coding standards
• Robustness: Prompt stability, fault tolerance, self-correction ability

Test Dataset Construction

Covers different difficulty levels and domains: basic algorithm problems, data structure problems, practical application problems, and system design problems.

Model Selection

Evaluate multiple mainstream open-source and closed-source commercial models to compare differences in prompt robustness.

Key Findings

• Widespread Prompt Sensitivity: All models show a pass rate fluctuation of over 20% across different prompt variants.
• Detailed Prompts Are Not Always Optimal: Overly detailed prompts may limit creativity; the optimal level is related to problem complexity.
• Quality of Examples Outweighs Quantity: Representative examples are more effective than multiple ordinary examples.
• Structured Prompts Are More Stable: Code comments and pseudocode formats improve model understanding.
• Differences in Model Robustness: Large models have better robustness, but medium-sized models can approach this through training strategies.

Sensitivity Analysis

Impact weights: Clarity of function description > Input/output specifications > Boundary condition explanations > Algorithm prompts.

Recommendations for Developers

1. Clearly define functional requirements; describe what to do rather than how to do it.
2. Provide at least one typical input-output example.
3. Explain boundary conditions and special cases.
4. Use structured formats such as lists and code blocks.
5. Iteratively optimize prompt expressions.

Recommendations for Model Developers

1. Include multiple prompts for the same problem in training data.
2. Evaluate model performance under prompt variations.
3. Analyze failure causes for specific prompt types.
4. Understand real user query habits.

Recommendations for Evaluation Systems

1. Incorporate prompt robustness testing into standard processes.
2. Multi-dimensional evaluation (correctness, quality, maintainability).
3. Use test cases from real development scenarios.
4. Track model stability performance over the long term.

Research Limitations

• Test problems are from algorithm competitions/exercises, which differ from industrial code.
• Only evaluates the Python language.
• Prompt variants are based on researchers' experience and do not cover all styles.

Future Directions

1. Develop automatic prompt optimization algorithms/tools.
2. Explore training methods to enhance model prompt robustness.
3. Comparative study of prompt variability across multiple languages.
4. Research on interactive code generation strategies.
5. Prompt optimization for specific domains (e.g., Web development).

This study reveals the significant impact of prompt variability on LLM code generation capabilities through rigorous experiments. The constructed composite evaluation framework provides a methodological reference for subsequent research. The proposed practical recommendations have guiding significance for developers and model designers. In today's era of widespread AI programming assistants, understanding model prompt sensitivity not only has academic value but is also crucial for practical applications. Effective communication with LLMs will become an essential skill for developers.