Software lifecycle services provider GitLab recently acquired UnReview, a machine learning (ML) tool that helps identify appropriate code reviewers, both to bring this functionality to its DevOps platform, as well as to further its overall mission to “build data science workload needs into the entire open DevOps platform,” according to a statement. On the first point, GitLab senior director David DeSanto explained that GitLab wants to: reduce friction, improve experience, better security, and generally increase efficiency.

“We have seen customers who end up having to merge requests that end up being open for sometimes weeks due to the manual process of code review. When you look at that as a process, that could be a very laborious process for the developer and for the reviewer. This happens because we’re all human,” said DeSanto. “Let’s say I’m making a change and as a developer, I go, ‘The right person to read this is Christy,’ I can then assign it to her. But what if she’s on vacation? What if she’s reviewing six or seven other merge requests? Now I’m sitting in this limbo state unable to move forward until I’m unblocked by her.”

UnReview remedies this situation using ML to best determine the most appropriate code reviewer at any given time, according to a set of characteristics, acting as a load balancer of sorts, as well as determining the best reviewer according to their history. DeSanto said that GitLab became interested in UnReview after trying the product themselves and finding out that it could improve their workflow.

“We did find that it was actually confirming we were selecting the right code reviewer at times but also finding out that we’ve selected the wrong code reviewer,” DeSanto said. “We were able to see the time improvement for ourselves inside of our own workflow, and that’s what really made us excited about the possibility of having UnReview join GitLab.

#devops #machine learning #ai #gitlab #unreview