GitLab unveils GitLab 17, AI for devsecops

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 influence 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 embrace code recommendations and code clarification, with enterprise-oriented AI capabilities for different points of the software program improvement life cycle, corresponding to detecting and fixing safety vulnerabilities and summarizing subject discussions and merge requests. GitLab Duo Enterprise can even resolve CI/CD bottlenecks and failures and improve workforce collaboration, GitLab stated.

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

GitLab additionally supplied an inventory 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 grasp person habits 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 knowledge scientists to develop AI/ML fashions on the identical platform the place engineers construct and deploy code.