Jessie Houghton, Oscar Obeso, and Rhea Patel detail how smarter AI edits were achieved in Visual Studio Copilot, discussing the move from heuristic-based to model-based approaches, speculative decoding, and improved code integration.

Smarter AI Edits in Visual Studio Copilot

Authors: Jessie Houghton, Oscar Obeso, Rhea Patel

When work began on smarter AI edits in Visual Studio Copilot, the team faced significant challenges. The goal was for Copilot to not only provide high-quality suggestions but to apply them seamlessly into existing code, handling complex scenarios and maintaining code integrity.

The Complexity of Implementing AI-Generated Edits

Early approaches to integrating AI edits used heuristics and rule-based techniques like string and pattern matching. While these occasionally succeeded, they were inconsistent—especially for edits involving multiple lines—and failed as Copilot expanded to support more languages and complex scenarios. Maintaining static rules could not keep up with evolving AI models; success rates plateaued around 50%.

Advancements with Modern AI: Speculative Decoding

Recent advances in AI have allowed for a fresh take on the challenge. Two key improvements drove this change:

  • Larger Model Context Windows: Enabled the AI to understand broader context for each change.
  • Speculative Decoding: This technique pairs a fast model with a more advanced model. The fast model predicts upcoming tokens (edit parts), while the advanced model steps in as needed for more accurate refinements. This collaborative approach has increased average token generation speed by 2-3x and improved the accuracy of edit integration.

Through speculative decoding, the process moved from rule-sets to an AI model simulating an “ideal” version of the code file. A smart diff algorithm compares this ideal with the actual file to precisely map and apply edits. This approach intelligently handles edge cases, overlapping code, and missing syntax.

Balancing Accuracy and Speed

Integrating sophisticated AI models introduced latency, as edits now depend on network calls and incremental token streaming. To counter this, a real-time streaming animation was added to Visual Studio, letting users observe edits being applied line by line. While this sacrifices some speed, user feedback affirms that the gains in precision and code quality outweigh the loss of immediacy.

Agent Mode and the Future

These improvements form the backbone for Agent Mode in Visual Studio Copilot, where the AI can directly assist in building, debugging, and testing code. For Agent Mode, precision and reliability of edits are crucial. Ongoing work includes faster implementations of speculative decoding and further advancements in token generation, aiming for even closer to real-time AI-powered coding assistance.

Key Takeaways:

  • Initial rule-based edit integration was unreliable for complex changes.
  • Speculative decoding brings faster, more reliable code edits by combining models.
  • Streaming visual feedback keeps users informed and confident in edits.
  • These enhancements underpin Agent Mode, which lets Copilot actively execute code improvements.

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