GitLab this week announced the general availability of GitLab Duo Chat, a natural language interface through which DevOps teams can invoke generative artificial intelligence (AI) to generate code, create tests and access summarizations of code. It is available as an add-on to the GitLab Duo Pro AI framework, which the company is embedding across its DevOps platforms. GitLab Duo Chat can be invoked via the GitLab user interface or GitLab’s web integrated development environment (IDE) in addition to third-party IDEs, including VS Code and the IDE provided by JetBrains.
Over the last few years, GitLab has been working with Google to embed a range of AI capabilities into its core platform. With the addition of GitLab Duo Chat, these are now more accessible.
As part of that effort, GitLab is also now adding privacy controls for GitLab Premium and Ultimate customers. These enable organizations to control sensitive data at the project, group, and subgroup levels to address security and compliance concerns.
David DeSanto, chief product officer for GitLab, said as AI capabilities become more accessible, the pace at which applications can be built and deployed is about to significantly accelerate. The most immediate impact should be a reduction in the number of instances where builds fail to deploy because the quality of the code used to construct them has improved, he added.
AI should make it easier for subject matter experts who have little software engineering expertise to participate in the application development process, noted DeSanto.
The immediate challenge DevOps teams will encounter is to determine how best to employ the prompts to generate code and tests within the context of their existing DevOps workflows. Over time, however, as the reasoning engines embedded within the large language models (LLMs) that are at the core genAI platforms improve, it will become feasible to automate a series of actions involving multiple tasks, DeSanto noted.
In the meantime, DevOps teams should start determining which manual tasks might be automated using a generative AI platform. In many organizations, there are still numerous bottlenecks in DevOps workflows that conspire to slow down the pace of application development.
AI platforms are unlikely to eliminate the need for application developers and software engineers any time soon. However, many scripts that DevOps teams have historically written themselves will soon be written and tested by machines. DevOps teams will soon find themselves supervising the development of more applications that, in addition to being constructed faster than ever, are less troublesome to update and maintain without having to add a small army of software engineers. The tradeoff is the overall size of the codebases that DevOps teams will be expected to manage is about to substantially increase, noted DeSanto.
In an era where organizations are more dependent than ever on software, the demand for applications already exceeds the ability of DevOps teams to address. AI should enable DevOps team to build and deploy applications at a level of scale that only a couple of years ago would have seemed unimaginable.
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