CircleCI has committed to adding additional collective intelligence capabilities to its continuous integration/continuous delivery (CI/CD) platform that will leverage machine learning and other forms of artificial intelligence (AI) to optimize application development and delivery.
CircleCI CEO Jim Rose said that as the company’s namesake CI/CD platform continues to collect more telemetry data from multiple customers, it becomes feasible to create AI models that will optimize everything from application testing to reliability and performance.
As part of that strategy, CircleCI has also added capabilities such as test splitting recommendations that employ machine learning algorithms to estimate the expected time savings of enabling parallelism on test jobs.
In addition, CircleCI is creating a team of analysts and data scientists to develop additional AI capabilities based on the data collected via the cloud version of the company’s platform.
The decision to invest more in developing AI capabilities follows the acquisition of Ponicode, a provider of a unit testing platform, earlier this year. That platform uses AI to augment application testers by providing capabilities such as syntax validation, on-hover hints and autocompletion capabilities to improve the accuracy of software developed using Visual Studio Code. The goal is to integrate that AI testing technology within the CircleCI platform to make it possible to apply algorithms to individual developers’ code as well as to code aggregated from multiple developers within a build as it is continuously updated across a DevOps workflow.
The workflows make up a software supply chain that, in many regards, functions like any other type of supply chain, noted Rose. As such, there is plenty of opportunity to apply AI models to optimize those workflows in the same way AI is being applied to optimize other types of supply chains, he added.
It’s still early days as far as applying AI to DevOps processes is concerned, but it’s clear there is a need to further automate a wide range of manual processes. It’s not likely that AI is going to replace the need for DevOps engineers any time soon, but it will reduce a lot of the toil that currently hinders productivity. The challenge, of course, is that it takes a lot of telemetry data to train an AI model, which typically can only be efficiently gathered and stored with a cloud platform. Less clear is whether AI capabilities might drive more organizations to shift away from legacy CI/CD platforms deployed in an on-premises IT environment where there might not be enough telemetry data available to effectively train an AI model.
The one thing that is certain is there is now a race to apply AI to DevOps workflows. Organizations that have adopted DevOps typically are highly committed to automation, but the amount of AI applied to those workflows has been limited thus far. In the coming year, however, chances are good the amount of AI being applied to DevOps workflows will steadily increase, especially as many DevOps teams struggle to keep pace with complex application environments.