In this article, Rob Bos walks through his experience leveraging GitHub Copilot to refactor a YAML pipeline script by extracting a script directly from the pipeline into a standalone file. Rob provides an end-to-end demonstration that includes sharing his prompts, the code generated by Copilot, and specific cases where Copilot did not fully meet his initial needs. Despite these shortcomings, Rob illustrates how follow-up questions and additional interactions with Copilot ultimately led to a successful outcome.

Overview

The article details Rob’s practical usage of GitHub Copilot as a productivity tool during a common development task: improving a GitHub Actions workflow by refactoring code out of the YAML definition. He documents each step, showing both Copilot’s strengths in generating boilerplate or repetitive code, as well as its limitations when the context becomes complex or when multiple steps are required.

Process Highlights

  • Starting Point: Rob begins with a script embedded within a GitHub Actions YAML pipeline file, which he intends to extract into a reusable, standalone script file.
  • Copilot Interaction: He inputs clear, contextual prompts in his editor, demonstrating how Copilot responds with suggestions for code extraction and refactoring.
  • Failure and Iteration: Copilot doesn’t always provide the desired result in the first attempt. Rob documents these shortfalls and shows how refining prompts or breaking down the task helps guide Copilot to a correct solution.
  • Result: With persistence and iterative guidance, Rob achieves a clean code refactoring, illustrating the effective synergy between Copilot’s AI assistance and human direction.

Supporting Materials

Rob mentions that slides accompany his video walkthrough. These slides serve as a self-contained resource, allowing readers who prefer documentation over video content to follow the step-by-step process.

Key Takeaways

  • Copilot can significantly accelerate routine code transformations in pipeline/workflow contexts.
  • Human guidance remains crucial, particularly when complex refactoring requires nuanced understanding.
  • Transparency about failures and incremental problem-solving is valuable for users seeking to get the most out of AI-based development tools.

This post appeared first on Rob Bos’ Blog. Read the entire article here