study: Gumdrop
Finding ways to make developer lives better is incredibly important to us. Our GUMDROP study looks to see improvements to GitHub Copilot’s code suggestions by improving model context.
Presented GitHub Universe 2024 by Cory Hymel
Improving Performance of Commercially Available AI Products in a Multi-Agent Configuration
Abstract - In recent years, with the rapid advancement of large language models (LLMs), multi-agent systems have become increasingly more capable of practical application. At the same time, the software development industry has had a number of new AI-powered tools developed that improve the software development lifecycle (SDLC). Academically, much attention has been paid to the role of multi-agent systems to the SDLC. And, while single-agent systems have frequently been examined in real- world applications, we have seen comparatively few real-world examples of publicly available commercial tools working together in a multi-agent system with measurable improvements. In this experiment we test context sharing between Crowdbotics PRD AI, a tool for generating software requirements using AI, and GitHub Copilot, an AI pair-programming tool. By sharing business requirements from PRD AI, we improve the code suggestion capabilities of GitHub Copilot by 13.8% and developer task success rate by 24.5% — demonstrating a real-world example of commercially-available AI systems working together with improved outcomes.
Created with Google’s NotebookLM
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