GitLab has unveiled GitLab 17, a serious replace of its devsecops platform that brings a CI/CD catalog of reusable pipeline elements and an AI affect dashboard. The corporate additionally introduced GitLab Duo Enterprise, an AI-powered assistant that helps detect vulnerabilities in code and resolve CI/CD bottlenecks.

GitLab Duo Enterprise, the topic of a digital launch occasion on June 24, combines the developer-focused AI capabilities of GitLab Duo Professional, which embody code recommendations and code rationalization, with enterprise-oriented AI capabilities for different elements of the software program growth life cycle, resembling detecting and fixing safety vulnerabilities and summarizing concern discussions and merge requests. GitLab Duo Enterprise may also resolve CI/CD bottlenecks and failures and improve crew collaboration, GitLab mentioned.

GitLab 17, launched Might 16, introduces a CI/CD catalog that enables GitLab customers to find, reuse, and contribute pre-built CI/CD elements. Customers can also create a personal catalog to distribute personalized pipelines to automate workflows.

The brand new AI affect dashboard in GitLab 17 is meant to assist organizations perceive the affect of the GitLab Duo AI programming assistant on developer productiveness. For instance, customers can examine AI utilization traits with software program growth metrics like lead time, cycle time, DORA, and vulnerabilities.

GitLab additionally supplied a listing of enhancements coming to the devsecops platform:

• A local secrets and techniques supervisor to permit customers to retailer delicate credentials.
• Static software safety testing (SAST) integrations to assist enhance accuracy, scale back false positives, and resolve application-layer dangers.
• Analytics capabilities to know consumer conduct patterns, measure product efficiency, and prioritize function enhancements.
• Enterprise agile planning capabilities, together with enhanced epics, customized fields in points, and roadmaps.
• A mannequin registry for information scientists to develop AI/ML fashions on the identical platform the place engineers construct and deploy code.