Can GitHub's Copilot AI put the fun back into being a developer?

Image: Getty/Hinterhaus Productions

On a mission to measure AI-assisted developer productivity, researchers at GitHub recently ran an experiment comparing coding speeds of a group using its Copilot code completion tool versus a group relying on human ability alone. 

GitHub Copilot is an AI pair-programming service that launched publicly earlier this year for $10 per user per month or a $100 per user per year. Since launching, researchers have been curious to know whether these AI tools really translate into a boost to developer productivity. The catch is that it's not easy to identify the right metrics to measure performance changes. 

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Copilot is used as an extension to code editors, such as Microsoft's VS Code. It generates code suggestions in multiple programming languages that users can accept, reject or edit. The suggestions are provided by OpenAI's Codex, a system that translates natural language to code and is based on OpenAI's GPT-3 language model. 

SEE: What is Coding and What is it Used For? A Beginner's Guide

Google Research and the Google Brain Team concluded in July, after studying the impact of AI code suggestions on over 10,000 of its own developers' productivity, that the debate over relative performance speed remains an "open question". That's despite concluding that a combination of traditional rule-based semantic engines and large language models, such as Codex/Copilot, "can be used to significantly improve developer productivity with better code completion". 

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