Why Did GitLab Acquire UnReview?


Last week, open-source DevOps lifecycle platform GitLab acquired machine learning (ML) startup UnReview for an undisclosed amount. UnReview automatically recommends appropriate code reviewers for pull requests of GitLab, GitHub and BitBucket projects. 

GitLab is a collaboration tool for software developers and DevOps. Started in 2014, GitLab Inc. has more than 1,300 remote team members spread across 68 countries. Currently, the platform consists of thousands of developers and has about 30 million+ users (both paid and free), from startups to global enterprises.


Sign up for your weekly dose of what's up in emerging technology.

“Integrating UnReview into the GitLab platform marks our first step in building GitLab’s applied ML for DevOps,” said Eric Johnson, CTO of GitLab. 

Further, he said incorporating machine learning into GitLab’s DevOps platform improves the UX by automating workflows and compressing cycle time across all areas of the DevSecOps lifecycle. “We are also building new MLOps features to empower data scientists,” he added. 

GitLab said the UnReview technology would be integrated into the GitLab code review experience for SaaS customers by the end of 2021. 

Why GitLabs acquired UnReview?

Accelerating development cycles while ensuring the highest quality code is one of the most common challenges DevOps teams face today. AI/ML tools such as UnReview, Tabnine (formerly Codota), Deepchecks, Granulate, help fast track DevOps cycles by anticipating what developers need in advance.

According to Gartner, 40% of DevOps teams are expected to be using application and infrastructure monitoring apps that have integrated artificial intelligence for IT operations (AIOps) by 2023. Also, an enterprise using AI-based tools reduces review time by over 50%, revealed Delloite, in a report ‘AI-assisted software development.’  

As per GitLab’s 2021 DevSecOps survey, 75% of respondents said their DevOps teams are planning to use ML/AI for testing and code review. The study revealed nearly 55% of operations teams said their DevOps life cycles were either completely or mostly automated. 

Jim Mercer, research director DevSecOps and DevOps at global market intelligence firm IDC, said, DevOps teams can capitalise on cloud solutions that provide machine learning to remove friction from the DevOps pipeline, optimising developer productivity. 

GitLab’s applied machine learning for DevOps: 

  • With the integration of UnReview into GitLab’s platform, the platform will enrich machine learning capabilities to speed up the software development lifecycle. 
  • Using UnReview’s machine learning algorithm, the merge request reviewers feature will be accelerated from a primarily manual process to an automated process. This will be extended to automate other workflow tasks like GitLab Epics soon. 
  • Improves experience with more intelligent machine learning backed features to automate portfolio management within the manage and plan stages. 

Towards unifying DevOps 

The UnReview acquisition leads with business value first and provides GitLab with centralized expertise to build data science workload needs into the entire open DevOps platform.

GitLab said this empowers developers, data scientists, and data engineers to be highly efficient, collaborative, and open while streamlining operations processes. Integrating this technology, along with GitLab’s machine learning expertise, builds the basis of GitLab’s long term strategy to meet data teams where they are today while also building a path to a ModelOps Stage in the DevOps toolchain.

“I look forward to enhancing the user experience by playing a role in integrating UnReview into the GitLab platform and extending ML/AI into additional DevOps stages in the future,” said Alexander Chueshev, UnReview founder and senior full-stack engineer at GitLab. 

More Great AIM Stories

Amit Raja Naik
Amit Raja Naik is a seasoned technology journalist who covers everything from data science to machine learning and artificial intelligence for Analytics India Magazine, where he examines the trends, challenges, ideas, and transformations across the industry.

Our Upcoming Events

Conference, in-person (Bangalore)
MachineCon 2022
24th Jun

Conference, Virtual
Deep Learning DevCon 2022
30th Jul

Conference, in-person (Bangalore)
Cypher 2022
21-23rd Sep

3 Ways to Join our Community

Discord Server

Stay Connected with a larger ecosystem of data science and ML Professionals

Telegram Channel

Discover special offers, top stories, upcoming events, and more.

Subscribe to our newsletter

Get the latest updates from AIM