In 2019, a software engineer — at Google or indeed anywhere else — would have heard of advances in machine learning, and how deep learning has become remarkably effective in fields such as computer vision or language translation. However, most of them would not have imagined, let alone experienced, the ways in which machine learning might benefit what they do.
Just five years later, in 2024, there is widespread enthusiasm among software engineers about how AI is helping write code. And a significant number of those have used ML-based autocomplete, whether it is using company internal tools at large companies, e.g., Google’s internal code completion, or via commercially available products.
In this blog, we present our newest AI-powered improvements within the context of the continuing transformation of Google’s internal software development tools, and discuss further changes that we expect to see in the coming 5 years. We also present our methodology on how to build AI products that deliver value for professional software development. Our team is responsible for the software development environments where Google engineers spend the majority of their time, including inner loop (e.g., IDE, code review, code search), as well as outer loop surfaces (e.g., bug management, planning). We illustrate that improvements to these surfaces can directly impact developer productivity and satisfaction, both metrics that we monitor carefully.