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Chat-oriented programming (CHOP) in action

Aug 20, 2024 - news.bensbites.com
The article discusses the concept of Chat-Oriented Programming (CHOP), a new programming methodology that leverages AI to streamline the coding process. CHOP, as exemplified by tools like Cody, allows developers to interact with their codebase in a conversational manner, asking questions and receiving answers in natural language. This approach can significantly enhance various stages of the software creation lifecycle, including understanding the codebase, writing and testing code, debugging, and maintaining code quality.

The author, a professional coder with over 15 years of experience, shares his insights on how CHOP has transformed his coding practices. He provides detailed examples of how he uses Cody, an AI coding assistant, to navigate and understand codebases, generate and edit code, debug issues, and maintain high-quality codebases. He concludes by acknowledging that while CHOP is still in its infancy, it holds significant potential for developers looking to incorporate AI into their workflows.

Key takeaways:

  • Chat-oriented programming (CHOP) is a new way of programming that involves coding via iterative prompt refinement. It is facilitated by AI coding assistants like Cody, which can help programmers understand, write, debug, and maintain their codebase more efficiently.
  • CHOP changes the traditional coding workflow by allowing developers to ask questions in natural language and get high-quality answers in seconds. This makes understanding a new or even a familiar codebase much more effective and efficient.
  • With CHOP, writing code becomes a collaborative process with the AI coding assistant. Developers can focus on the end result and let the AI handle writing the code. This approach can greatly improve Test-Driven Development (TDD) by allowing developers to define both the test cases and implementation logic in natural language.
  • CHOP also aids in debugging code issues. The AI coding assistant can analyze error messages, suggest potential causes, and even generate the fix. Furthermore, it can help maintain a high-quality codebase by identifying potential problems, suggesting improvements, writing unit tests, and refactoring code to match guidelines.
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