GitLab introduced a new set of software development platforms with features that will help companies improve their cybersecurity, build machine learning applications, and more easily troubleshoot errors.
GitLab provides a popular platform that companies use to manage their software development projects, helping developers manage the code files that comprise an application. Over the years, GitLab added features to automate numerous related tasks such as testing code for security vulnerabilities, deploying software to production, and detecting application errors.
The new platform release that GitLab announced will enhance the company’s value proposition in several areas. One particularly central focus of the update is cybersecurity.
DAST (dynamic application security testing) is used to scan applications for vulnerabilities. The method involves launching simulated cyberattacks against an application to determine if it may be vulnerable to hacking. GitLab is replacing the open-source DAST tools that its platform has used for the task so far with a proprietary engine designed to provide better performance and more configuration options.
Gitlab is also rolling out a feature that can automatically generate a list of all the software components in an application, which will ease cybersecurity evaluations. It will enable developers to host the code that they produce as part of a software development project in a secure cloud environment instead of on a local computer to reduce the risk of cyberattacks.
GitLab plans to roll out a tool that will enable developers to create different versions of a machine learning model, compare them and determine which is most effective. Another upcoming feature will ease the task of managing the datasets that a software team uses to train neural networks. The feature can help move training data from external systems to GitLab’s platform.
By the acquisition of startup Opstrace last year, an open-source tool that can be used to troubleshoot application errors. GitLab is adding new features that will enable developers to more easily analyze different types of error data, including metrics, logs, and traces, as part of troubleshooting efforts.