Code Generation
A technology in which AI automatically writes programming code based on natural language descriptions or the context of existing code.
Detailed explanation
Why it matters in tool selection
Code generation tools range widely, from simple autocompletion models to agentic models that read the entire project context and modify multiple files. Depending on the depth of automation, productivity gains and the review burden will vary. To safely adopt these tools in a team environment, you must check how internal code is trained or transmitted, security policies, the licensing of generated code, and IDE integration.
What to check when choosing a tool
- Does it integrate well with your editor (IDE), languages, and frameworks?
- Is internal code transmitted or used for training externally, and is there a non-transmission option?
- Is it simple autocompletion or an agentic type that modifies multiple files?
- Does it support or specify security and license review for the generated code?
Real-world application example
If a developer instructs, "Write a unit test for this function," the tool reads the function signature and logic to generate test code, including boundary values and exception cases. However, humans must verify whether the generated tests cover all actual intentions, and using it with this review process in mind is necessary for safety.