In the article "Exploring Gen AI: Copilot's new multi-file editing," Martin Fowler discusses the application of generative AI, specifically large language models (LLMs), in software delivery practices, particularly for migrating tech stacks, such as moving from Enzyme tests to the React Testing Library. Leveraging Microsoft’s Autogen tool, Fowler outlines an experimental approach where he develops an AI agent capable of autonomously performing coding tasks based on user input and pre-defined functions. His observations reveal both successes and failures in the AI's performance, highlighting the current limitations of generative AI in coding, such as syntax errors during automated changes and a growing context in iterative requests. Despite these challenges, he suggests that focused use cases, like tech stack migrations, could benefit significantly from AI assistance in the near future.

Key Points:

• Generative AI and LLMs are becoming prominent in software development.
• Fowler's role involves coordinating efforts to understand AI's impact on software delivery.
• An experiment was conducted to migrate Enzyme tests to React Testing Library using Autogen.
• Agents in this context act autonomously based on output from LLMs.
• The migration included several manual steps which were replicated by the AI.
• The experiment highlighted both effective tool usage and significant failures in AI execution.
• Limitations included code syntax errors and increasing request sizes due to context retention.
• The author advocates for focusing on specific problem areas for AI application, such as tech stack migrations.
  Link to Article