This was the year that saw GitHub Copilot move from a plug-in on Jetbrain, where it was first launched in 2021, to broad availability for the Visual Studio IDE in March. It was followed by the release of Amazon's code completion service, Code Whisperer, in June, and Replit's Ghostwriter in October. Tabnine, an AI startup for code generation, secured $15.5 million in funding, while another code-completion startup, Kite, died in the wake of Copilot's popularity. And then, too, by the end of the year, it all ended up as a big question mark when GitHub wound up in litigation over its use of open source repositories in Copilot. Although much of the focus in 2022 was on automated coding and code completion, it turns out that AI technologies transformed code in more subtle ways in the past year. "We don't believe we're going to see AI replace DevOps engineers or platform engineers, but really augment them," said Zach Zaro, co-founder and CEO of Coherence, a DevOps automation startup that leverages AI. "You have a lot happening at the application layer level -- AI coming to help developers write application code, not infrastructure code."

Developers today have a fantastic assortment of tools and technology available to them, which they use to construct the digital world around us. However, the sheer number of choices in the DevOps and CICD toolchains introduces a vast amount of complexity, which leads to multiple inefficiencies. Now a new discipline called developer productivity engineering (DPE) is emerging to tackle this problem, and advanced analytics and AI play big roles. While the advent of DevOps and continuous integration, continuous deployment (CICD) methods has made developers lives simpler in many respects, they have also unleashed new forces that hinder developer productivity, according to Hans Dockter, the CEO of Gradle, the for-profit company behind the leading open source build tool of the same name. For starters, testing is critical to ensure software is bug free and doesn't pose a security risk.