GitLab has updated its DevSecOps platform to version 17, a step that adds a continuous integration and distribution (CI/CD) catalog of reusable components, as well as a panel for the impact of AI. Additionally, GitLab has announced the Duo Enterprise launchan AI-powered assistant that helps developers detect vulnerabilities in code and address bottlenecks in continuous integration and continuous delivery processes.
GitLab Duo Enterprise, launching on June 24, combines the developer-focused AI capabilities of GitLab Duo Pro with more enterprise-oriented AI features. In this way, it offers both suggestions and explanations about the code with supports for other aspects of the software development life cycle. Among them the detection and solution of security vulnerabilities and the summary of discussions on specific topics. Additionally, GitLab Duo Enterprise will improve collaboration between development team members.
As for GitLab 17, it is available now, and includes a continuous integration and distribution catalog that allows users of the platform to discover, reuse and contribute to previously developed CI/CD components. GitLab users will also be able to generate a private catalog with this version to distribute custom channels to automate workflows.
The new AI Impact dashboard in GitLab 17 is designed to help companies understand the impact that the GitLab Duo AI programming assistant has on developer productivity. With it, for example, AI use trends can be compared with different software development metrics.
In addition, it also has a native secrets manager, which allows users to store sensitive credentials, with the aim of protecting access to different accounts and services. On the other hand, GitLab 17 has SAST integrations, for security testing of static applications. Its mission is to help improve accuracy, reduce the number of false positives, and resolve application layer risks.
Its analytical capabilities provide the opportunity to understand user behavior patterns, measure product performance, and prioritize improvements for the future. As for agile planning for companies, they include roadmaps, wikis and key results (OKRs). It also offers a model registry for data scientists that allows them to develop AI and machine learning models on the same platform used to develop and deploy code.
GitLab 17 also incorporates new observability functionswith which development and operations teams will be able to understand the impact on the application of a code or a configuration change through error logging, distributed tracing, metrics and logs.