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Thanks for visiting this category direction page on Code suggestions in GitLab. This page belongs to the Code Creation group of the Create stage and is maintained by Kevin Chu (kchu at gitlab dot com).
Code suggestion systems use Generative AI to suggest relevant code snippets and autocomplete code while software developers write code. These systems aim to boost programmer productivity and reduce time spent on rote coding tasks. Some key points about AI code suggestion systems:
Be the best-in-class enterprise code suggestion solution for end-to-end DevSecOps by helping increase engineering productivity without sacrificing security, privacy, and enterprise control.
There are more details on how we developed the strategy in this confidential (internal to GitLab) epic.
Our short-term (3 months) goal is to launch a generally available code suggestion product as soon as feasibly possible.
As every sector aims to gain a competitive edge with AI, GitLab must supplement our product suite with AI-powered workflows. Code suggestions are among the most obvious (even if it is not the biggest SDLC efficiency driver) and mature AI categories. By empowering customers to adopt code suggestions, we can then jointly prioritize and augment the remaining DevSecOps workflow with AI.
If we don't move quickly, we risk alienating customers who need to adopt AI immediately. The current AI hype cycle makes these needs so strong that they pose significant churn risk. By delivering code suggestions, we can mitigate this risk while collaborating with customers to determine other high-impact AI integrations.
For a GA product, GitLab Duo Code suggestions will have the following attributes:
Between Beta and GA, we are focused on the following:
Longer-term, we plan to position code suggestions, along with the rest of our AI offerings, to compete on the axis of privacy, security, and enterprise control. In addition, we plan to continue to improve the quality of suggestions, because after all, if code suggestions are too limited or of a subpar quality, none of the other strategic themes would matter. Code suggestions should at least:
Code is enterprise IP. How we enable privacy without compromise even as new AI-powered features become available is a top concern for organizations. Data collection and management, access control, authorization, secure communication, compliance, governance, responsible AI, and other ethical consideration will be strategic vectors we focus on.
One of the potential risk to AI can be introducing vulnerability thorough suggested code, or perpetuation of security problems. Instead of treating code suggestion as more potential risk, we plan to leverage the rest of the DevSecOps capabilities to make suggestions that improves our customer's security posture.
Managing feature access at scale is an important problem to solve for enterprises. Furthermore, providing visibility into how code suggestions is used, how code suggestion impacts overall productivity will help GitLab customers make data-informed decisions.
People who code:
In the future, we may expand to include 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.
The main body of work we are currently focused on is improving the quality of code suggestions. This effort is captured in the code suggestion evolution and its sub-epics and issues. The table below highlights some of the most important needle movers as we get ready for GA.
|Best in class model||Acceptance Rate||Introduce Anthropic||FY24'Q3|
|Improve code suggestion quality||Acceptance Rate||Improve formatting, Reduce code suggestion requests, Increase when we use code-generation||FY24'Q4 FY25'Q1|
|Operational Readiness||Ready to support Code Suggestions GA||Identifying projects in issue||FY24'Q4|
|Enterprise Control||?||Granular control to enable/disable Code Suggestions in Project/Groups/Sub-Groups/Users||FY25'Q1 FY25'Q2|
|Performance||Acceptance Rate||Streaming||FY24'Q4 FY25'Q1|
|Smarter context||Acceptance Rate||Repository X-Ray, Increase context window||FY25'Q1 FY25'Q2|
|Chat||Acceptance Rate, Number of users||Code Tasks||FY25'Q1 FY25'Q2|
We plan to invest according to our long-term strategy. Here are some loosely prioritized projects that we may work on over the next 6-12 months.
This category is currently at minimal maturity. At GA we may move the category to viable maturity.
Please see the content in our internal handbook