Gitlab code suggestions

Overview

GitLab's AI-Assisted Code Suggestions is an innovative tool which aims to improve coding productivity by offering predictive completions for code blocks, defining function logic, generating tests, and suggesting common code like regex patterns, all within a familiar coding environment.

With the main focus on privacy, GitLab guarantees that non-public customer code doesn't serve as part of the training data for this feature. The tool also has multilingual support, offering AI-powered code suggestions in 14 languages, including C++, C#, Go, Google SQL, Java, JavaScript, Kotlin, PHP, Python, Ruby, Rust, Scala, Swift, and TypeScript.

The service also integrates well with different IDEs, as GitLab extensions can be found in popular IDE marketplaces. These include the GitLab Web IDE, VS Code, Visual Studio, Jetbrains-based IDEs, and NeoVIM.

Additionally, GitLab's Code Suggestions is available for self-managed GitLab instances via a secure connection to GitLab.com. The tool continually improves the quality of suggestions with new prompt engineering, intelligent model routing, and expanded contexts for inference windows.

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Initial release

June 29, 2023

Initial release of Gitlab code suggestions.

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$29/month

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Monthly

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Pros and Cons

Pros

Enhances coding productivity

Accelerates software development

Completes lines with one keystroke

Quickly starts functions

Generates boilerplate code

Generates tests

Prioritizes data privacy

Prioritizes data security

Keeps source code protected

Doesn't use code as training data

Uses open-source pre-trained models

Models are continually fine-tuned

Customized open-source dataset

Supports multi-language coding

Supports broad range IDEs

Planning to extend capabilities

Code suggestions for self-managed instances

Vast language support

Data privacy guarantee

Coming support for additional IDEs

Integrated IDE support

Improving suggestion quality

Expanded contexts for inference

Interactive demo

Ease of use

Continual expansion plans

Jetbrains support

VS Code support

Web IDE support

Visual Studio support

Ruby support

Swift support

Scala support

Rust support

Python support

Kotlin support

JavaScript support

Java support

Google SQL support

Go support

C# support

C++ support

TypeScript support

NeoVIM support

View 39 more pros

Cons

Supports only 13 languages

Limited control over suggestions

Limited to GitLab cloud infrastructure

Static suggestion presentation

Doesn't use customer's code for training

Requires secure connection for self-managed instances

Limited IDE support

No retention of source code inference

View 3 more cons

Q&A

What features does GitLab's AI-assisted Code Suggestions offer?

GitLab's AI-assisted Code Suggestions offers features such as predictive completions for code blocks, defining function logic, generating tests, and suggesting common code like regex patterns. It also supports multi-language coding and environment-specific IDEs.

Can GitLab's AI-assisted Code Suggestions complete entire lines of code?

Yes, GitLab's AI-assisted Code Suggestions can complete entire lines of code. This feature enhances coding productivity significantly.

How does GitLab's AI-assisted Code Suggestions prioritize data privacy and security?

GitLab's AI-assisted Code Suggestions prioritizes data privacy and security by ensuring that proprietary source code is protected and not used as training data. The source code inference against the Code Suggestions model is not retained within GitLab's enterprise cloud infrastructure.

What type of data does GitLab's AI-assisted Code Suggestions use as training data?

GitLab's AI-assisted Code Suggestions uses open-source pre-trained models, fine-tuned with a customized open-source dataset as training data. It does not use proprietary source code as part of its training data.

How does GitLab's AI-assisted Code Suggestions support multi-language coding?

GitLab's AI-assisted Code Suggestions supports multi-language coding by continuously fine-tuning its open-source pre-trained models with a customized open-source dataset. This allows it to provide intelligent code suggestions for multiple programming languages.

What programming languages does GitLab's AI-assisted Code Suggestions support?

GitLab's AI-assisted Code Suggestions supports 14 programming languages including C/C++, C#, Go, Google SQL, Java, JavaScript, Kotlin, PHP, Python, Ruby, Rust, Scala, Swift, and TypeScript.

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Will GitLab expand the capabilities of Code Suggestions in the future?

Yes, GitLab does have plans to expand the tool's capabilities in the future. They are working on providing Code Suggestions for self-managed instances through a secure connection to GitLab.com. They also plan to improve the user experience by offering support for additional IDEs and enhancing how suggestions are presented and accepted within IDEs.

Does GitLab's AI-assisted Code Suggestions offer support for additional IDEs?

Yes, GitLab's AI-assisted Code Suggestions will be offering support for additional IDEs such as JetBrains IntelliJ-based IDEs and Visual Studio.

How does GitLab plan to enhance how suggestions are presented within IDEs?

GitLab plans to enhance how suggestions are presented within IDEs by improving the suggestion quality with new prompt engineering, intelligent model routing, and expanded contexts for inference windows. This will give developers more control over the feature.

Does GitLab's AI-assisted Code Suggestions use proprietary source code as part of its training data?

No, GitLab's AI-assisted Code Suggestions doesn't use proprietary source code as part of its training data. The service is built with privacy-first in mind and non-public customer code doesn't serve as training data.

How does GitLab's Code Suggestions integrate with different IDEs?

GitLab's Code Suggestions integrates well with different IDEs through GitLab extensions that can be found in popular IDE marketplaces. Currently, it is compatible with the GitLab Web IDE, VS Code, Visual Studio, Jetbrains-based IDEs, and NeoVIM.

Can GitLab's Code Suggestions be used for self-managed GitLab instances?

Yes, GitLab's Code Suggestions can be used for self-managed GitLab instances via a secure connection to GitLab.com.

How does GitLab's AI-assisted Code Suggestions improve the quality of its suggestions?

GitLab's AI-assisted Code Suggestions improves the quality of suggestions with new prompt engineering, intelligent model routing, and expanded contexts for inference windows. They are continuously working on these enhancements.

What is the focus of GitLab's AI-assisted Code Suggestions?

The focus of GitLab's AI-assisted Code Suggestions is to empower developers by providing intelligent code suggestions, optimizing coding efficiency, and ensuring data privacy and security.

Are GitLab's AI-assisted Code Suggestions available in multiple languages?

Yes, GitLab's AI-assisted Code Suggestions are available in multiple languages. The service currently supports AI-powered code suggestions in 14 languages.

What languages does GitLab's AI-assisted Code Suggestions support?

GitLab's AI-assisted Code Suggestions supports 14 languages: C++, C#, Go, Google SQL, Java, JavaScript, Kotlin, PHP, Python, Ruby, Rust, Scala, Swift, and TypeScript.

Can GitLab's AI-assisted Code Suggestions function within different IDEs?

Yes, GitLab's AI-assisted Code Suggestions can function within several IDEs. It supports GitLab Web IDE, VS Code, Visual Studio, Jetbrains-based IDEs, and NeoVIM.

Is GitLab's AI-assisted Code Suggestions available for self-managed instances?

Yes, GitLab's AI-assisted Code Suggestions is available for self-managed GitLab instances via a secure connection to GitLab.com.

Does GitLab's AI-assisted Code Suggestions predictively complete code blocks?

Yes, GitLab's AI-assisted Code Suggestions does predictively complete code blocks, helping to enhance productivity for developers.

How does GitLab plan to improve the suggestion quality of Code Suggestions?

GitLab plans to improve the suggestion quality of Code Suggestions by continuously enhancing its prompt engineering, intelligent model routing, and expanded contexts for inference windows. They are constantly making improvements based on feedback and research.

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