
GitLab Inc., the company that offers the single application for the DevOps lifecycle, announced it has acquired UnReview, a machine learning (ML) based solution for automatically identifying appropriate expert code reviewers and controlling review workloads and distribution of knowledge. This acquisition is expected to advance the user experience within GitLab’s Dev Section including Manage, Plan and Create stages by improving a user’s ability to perform impactful code reviews by using ML to recommend code reviewers based on their previous contributions to areas of code as well as current reviewer workload. With this, teams can increase their velocity, code quality and security.
“Integrating UnReview’s technology into the GitLab platform marks our first step in building GitLab’s Applied Machine Learning for DevOps,” said Eric Johnson, CTO of GitLab. “By continuing to incorporate machine learning into GitLab’s open DevOps platform, we are improving the user experience by automating workflows and compressing cycle times across all stages of the DevSecOps lifecycle. We’re also building new MLOps features to empower data scientists.”
Based on GitLab’s 2021 DevSecOps survey, 75% of respondents report their DevOps teams are either using or planning to use ML/artificial intelligence for testing and code review. Additionally, a majority (55%) of operations teams report their life cycles were either completely or mostly automated. These statistics validate the importance of GitLab’s Applied Machine Learning for DevOps, integrating automation and machine learning technology like UnReview into the GitLab platform. With the addition of UnReview, many existing features within the Create stage will be enriched with machine learning capabilities to speed up the software development lifecycle. The merge request reviewers feature will be accelerated from a primarily manual process to an automated process using UnReview’s novel machine learning algorithm, which will also be extended in the future to automate other workflow tasks such as the triage of epics and issues including the assigning of issues and suggesting related issues and epics. Within the Manage and Plan stages, the UnReview technology provides an improved experience with more intelligent machine learning backed features to automate portfolio management.
Industry analyst research into successful operationalisation of machine learning outlines the many challenges organisations face by adopting point solution technologies. This is contrasted with the business value provided by integrating applied machine learning, DataOps, MLOps, and ModelOps into existing DevOps processes. The UnReview acquisition leads with business value first and provides GitLab with centralised expertise to build data science workload needs into the entire open DevOps platform. 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 active hiring around 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.
“With the rapid increase in cloud adoption, spurred by the COVID-19 pandemic, we’re seeing increased demand for cloud-enabled DevOps solutions,” said Jim Mercer, research director DevOps and DevSecOps at IDC. “DevOps teams who can capitalise on cloud solutions that provide innovative technologies, such as machine learning, to remove friction from the DevOps pipeline while optimising developer productivity are better positioned to improve code quality and security driving improved business outcomes.”
The UnReview technology will be integrated into the GitLab Code Review experience for GitLab SaaS customers by the end of year.
“I am grateful for the opportunity to share my passion for data science and machine learning with GitLab and its community,” said Alexander Chueshev, UnReview founder and senior full-stack engineer at GitLab. “I look forward to enhancing the user experience by playing a role in integrating UnReview into the GitLab platform and extending machine learning and artificial intelligence into additional DevOps stages in the future.”


Archive
- October 2024(44)
- September 2024(94)
- August 2024(100)
- July 2024(99)
- June 2024(126)
- May 2024(155)
- April 2024(123)
- March 2024(112)
- February 2024(109)
- January 2024(95)
- December 2023(56)
- November 2023(86)
- October 2023(97)
- September 2023(89)
- August 2023(101)
- July 2023(104)
- June 2023(113)
- May 2023(103)
- April 2023(93)
- March 2023(129)
- February 2023(77)
- January 2023(91)
- December 2022(90)
- November 2022(125)
- October 2022(117)
- September 2022(137)
- August 2022(119)
- July 2022(99)
- June 2022(128)
- May 2022(112)
- April 2022(108)
- March 2022(121)
- February 2022(93)
- January 2022(110)
- December 2021(92)
- November 2021(107)
- October 2021(101)
- September 2021(81)
- August 2021(74)
- July 2021(78)
- June 2021(92)
- May 2021(67)
- April 2021(79)
- March 2021(79)
- February 2021(58)
- January 2021(55)
- December 2020(56)
- November 2020(59)
- October 2020(78)
- September 2020(72)
- August 2020(64)
- July 2020(71)
- June 2020(74)
- May 2020(50)
- April 2020(71)
- March 2020(71)
- February 2020(58)
- January 2020(62)
- December 2019(57)
- November 2019(64)
- October 2019(25)
- September 2019(24)
- August 2019(14)
- July 2019(23)
- June 2019(54)
- May 2019(82)
- April 2019(76)
- March 2019(71)
- February 2019(67)
- January 2019(75)
- December 2018(44)
- November 2018(47)
- October 2018(74)
- September 2018(54)
- August 2018(61)
- July 2018(72)
- June 2018(62)
- May 2018(62)
- April 2018(73)
- March 2018(76)
- February 2018(8)
- January 2018(7)
- December 2017(6)
- November 2017(8)
- October 2017(3)
- September 2017(4)
- August 2017(4)
- July 2017(2)
- June 2017(5)
- May 2017(6)
- April 2017(11)
- March 2017(8)
- February 2017(16)
- January 2017(10)
- December 2016(12)
- November 2016(20)
- October 2016(7)
- September 2016(102)
- August 2016(168)
- July 2016(141)
- June 2016(149)
- May 2016(117)
- April 2016(59)
- March 2016(85)
- February 2016(153)
- December 2015(150)