How good is the generated code

As a large language model (LLM), the quality of its code generation depends on multiple factors, including the complexity of the task, the rigor of the code logic, and the ability to understand the context. Here are some pros and cons of DeepSeek code quality:

advantage:

1. Clear code structure

• Code usually follows a reasonable programming style and best practices.

• Variable naming is relatively standardized and code readability is high.

2. The basic logic is correct

• Suitable for common programming tasks, such as algorithm implementation, data processing, web development, etc.

• The code performs expected functions better and reduces syntax errors.

3. Multilingual support

• It can generate code in multiple languages ​​such as Python, JavaScript, C++, and Java.

• Suitable for different development scenarios, such as front-end, back-end, data science, etc.

4. Strong code completion ability

• Based on existing code, more accurate automatic completion can be provided.

• Suitable for assisting development and improving programming efficiency.

shortcoming:

1. Complex tasks are prone to errors

• There may be implicit errors for code involving multi-layer logic, concurrency, and performance optimization.

• Code that cannot be run directly or needs to be corrected may be generated.

2. Inadequate security considerations

• Code may be generated with security vulnerabilities such as SQL injection and XSS.

• Best practices may be lacking for security-related content such as password storage and encryption.

3. Missing contextual understanding

• In long code snippets, previously defined variables or logic may not be understood accurately.

• Code generation may require multiple iterations to meet the requirements.

4. Insufficient test coverage

• Code usually does not automatically generate unit tests or error handling logic.

• Developers need to verify the correctness of the code themselves.

Applicable scenarios

• Basic code generation (such as Python scripts, SQL queries, API calls)

• Code snippet completion (such as automatic filling functions, implementation of interfaces)

• Algorithm implementation (such as sorting, searching, data structure)

• Assisted learning (quickly get sample code)

in conclusion

DeepSeek code quality performs well on basic tasks , but there is still room for improvement in complexity, contextual understanding, and security . When using their code, manual inspection and testing is recommended to ensure that the code quality meets the requirements.