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Voxxed Days Ioannina 2024 - Things I learned after a decade of writing code generators
Discover the trade-offs and best practices of code generation after a decade of experience, exploring the limitations, opportunities, and strategies for effective code generation in software development.
- Code generation has its limitations, such as complexity, verbosity, and difficulty in maintenance.
- Specialized generators are often verbose and have more severe limitations, but dedicated tools like Codium and GitHub Copilot can enhance the code generation process.
- Code generation should be done sparingly, as it can lead to inconsistent and hard-to-debug code.
- Semantic versioning is an effective way to communicate changes in software development.
- It’s crucial to have a strategy for tracking changes and detecting defects in generated code.
- AI and LLMs can be used in code generation, but may not always produce optimal results.
- Code completion and debugging tools can be helpful in code generation.
- Fluent builders and DSLs can simplify the generation of code.
- Templates can be simple to read and use, but may require additional processing and logic to generate code.
- It’s essential to have a clear understanding of the development process and the level of code complexity required.
- Version control and testing are critical when using code generation tools.
- AI and LLMs may not always generate code that is optimal or easy to maintain.
- Code generation should be done only when necessary, and not as a substitute for manual coding.
- Code generators can be used to automate repetitive tasks and improve development efficiency.