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Stage | ai_assisted |
Maturity | minimal |
Content Last Reviewed | 2023-02-01 |
Thanks for visiting this category direction page on Code suggestions in GitLab. This page belongs to the AI Assisted group of the ModelOps stage and is maintained by Neha Khalwadekar (nkhalwadekar at gitlab dot com).
This direction page is a work in progress, and everyone can contribute. We welcome feedback, bug reports, feature proposals, and community contributions..
We intend to make code suggestions available for any external IDE and terminal for ultimate users, starting with GitLab's VS Code plugin and new Web IDE. GitLab aspires to build AI based solutions to increase developer productivity by helping them code faster and more efficiently. Get code suggestions that match a repository context and style conventions, and cycle through different options to decide what to accept, reject, or edit.
Initially we are focused on traditional developer personas including:
In the future we may expand to security personas to help write more secure code and review code for security vulnerabilities and fix them early in the software development lifecycle (SDLC), before you commit.
By implementing AI Assisted Code Suggestions in integrated development environments (IDEs), we believe that the software development lifecycle will be transformed in the following ways:
Overall, the integration of AI Assisted Code Suggestions should greatly improve the efficiency, quality, and speed of the software development process.
Revolutionizing the software development process through the integration of AI-assist code suggestions, resulting in increased productivity, reduced errors, and improved overall software quality.
In the near-term we plan on expanding beyond our initial gated customers ( Closed Beta Release ) and intend to iterate toward an Open Beta.
Currently we are not developing a GitLab trained model, however that is something we may consider in the future. For now we will focus on open source code generation models.
This category is currently at minimal maturity. We will share further maturity plans as we receive feedback from our first beta experiences in the coming milestones.
We plan to measure the success of this category based on the the following metrics:
As this category is new, we are still completing our evaluation of the competitive landscape.
As this category is new, we are actively engaging analysts on new reports and research, we'll share links to those as they are published