Understanding Limitations, Risks, and Common Pitfalls

Quality and Maintainability of Generated Code

While AI-generated code can be useful, it may not always meet the same standards as code written by an experienced developer. The generated code might be inefficient or hard to read, and may even contain functional issues. This drives up long-term maintenance costs and increases technical debt.

In addition, generated code may lack proper documentation and comments, making it difficult for other developers to understand its functionality. It's essential to document the code properly and add comments to improve readability and maintainability.

Performance Considerations

Generated code might not be optimized for performance. Developers who understand the performance characteristics of the involved systems must review and optimize the generated code to ensure it meets the required performance standards, especially in resource-intensive applications.

Security Risks

Generated code might introduce security vulnerabilities. Developers need to thoroughly review the generated code to ensure it follows security best practices and doesn't introduce any vulnerabilities like injection attacks, authentication flaws, or data leaks. It is also important to be aware of any libraries or other dependencies the generated code relies on. The need for regular vulnerability and dependency scans (ideally on every commit) is amplified for projects which will contain generated code.

Deskilling and Over-reliance

There is a cultural risk of developers becoming overly reliant on AI-generated code, which can lead to a dependency on the AI tool rather than fostering problem-solving and coding skills. This is especially true for less experienced developers who may not yet have solidified their own skills and opinions. It is important to thoroughly review and understand generated code, and to find other ways to get the benefits of AI assistance while still developing one's own coding abilities.

Bias and Ethics

AI models trained on existing code repositories may inadvertently perpetuate biases present in the training data. Developers should be mindful of potential bias in the generated code, especially when dealing with sensitive topics or applications.