Blog AI/ML How AI-assisted code suggestions will advance DevSecOps
March 23, 2023
5 min read

How AI-assisted code suggestions will advance DevSecOps

In this second blog in our ‘Future of AI/ML in DevSecOps’ series, find out the impact of AI Assisted code suggestions on the software development lifecycle.


This blog post is part of an ongoing series about GitLab's journey to build and integrate AI/ML into our DevSecOps platform. The series starts here: What the ML is up with DevSecOps and AI?. Throughout the series, we'll feature blogs from our product, engineering, and UX teams to showcase how we're infusing AI/ML into GitLab.

Artificial intelligence (AI) and machine learning (ML) have made incredible technological strides and are now poised to impact the software development process. As we can see, AI code suggestion proposals have already had a tremendous influence in helping programmers reduce repetitive tasks. AI-assisted code suggestions will enable developers to speed up coding, debugging, refactoring, documentation, and many more tasks, greatly enhancing the software development lifecycle (SDLC).

Trends adopting AI/ML from GitLab's DevSecOps Survey

What are suggestions for AI-assisted code?

ML techniques are used in AI-assisted code suggestions to assess code and recommend improvements. These recommendations involve modifying the syntax, streamlining the organization of the code, or suggesting more effective methods. By lowering errors, increasing effectiveness, and providing optimization advice, the aim is to assist developers in writing better code faster.

Animated gif image of code suggestions

How can AI-assisted code suggestions help?

AI-assisted code suggestions can substantially improve the programming experience by reducing errors and helping programmers write code faster, which will help reproduce the much higher production code quality.

Here are some of those SDLC improvements:

  • Decreased errors, increased accuracy. The capacity of AI-assisted code suggestions to decrease errors and increase accuracy is a critical advantage over manually written code. Developers can prevent common syntax errors, organize their code better, and boost algorithm performance with code suggestions. This leads to more dependable and effective code, which produces fewer defects and higher-quality software.
  • A rise in productivity. AI-assisted code suggestions can increase developers' efficiency by producing better code faster and more efficiently, saving time and money. Additionally, code suggestions can automate repetitive activities like formatting code, freeing engineers to concentrate on more complex jobs.
  • Improved collaboration. AI-assisted code recommendations can improve developer collaboration. Code suggestions can ensure all developers are on the same page by offering consistent coding standards and ideas for improvement. This will lessen the possibility of mistakes and facilitates efficient teamwork.
  • Faster rollout and iteration. AI-assisted code recommendations can hasten the deployment and iteration processes. With fewer errors and more effective code, developers can iterate and release updates faster. Code reviews also are faster and more efficient. As a result, enterprises can quickly bring new features to market, providing them with a substantial competitive edge.

GitLab’s competitive advantages

GitLab’s unified DevSecOps platform enables businesses to deliver software more quickly and efficiently while enhancing security and compliance and maximizing the total return of investment on software development. We anticipate GitLab AI Assisted Code Suggestions will extend and amplify these benefits to improve developer productivity, focus, and innovation without context switching and within a single DevSecOps platform using the GitLab Workflow VS Code extension to get code suggestions as they type. Depending on the user prompts, the extension provides entire code snippets like generating functions or completing the current line. Simply pressing the tab key enables you to accept the suggestions.

As AI technologies advance in sophistication, they will provide more individualized and nuanced ideas, increasing their value to programmers.

The low-code/no-code development sectors are where AI-assisted code suggestions are anticipated to have substantial impact. As these development platforms spread, we envision bringing AI-powered tools that can offer recommendations and optimizations to simplify the software creation and deployment process for non-technical users on

The following are some of the critical jobs we intend to address for our customers with AI Assisted Code Suggestions in the DevSecOps Platform:

  • Code optimization. How can we drastically reduce the time and effort required for developers to examine and test their code by identifying redundant or inefficient lines of code and suggesting streamlined alternatives?
  • Automatic bug detection and patching. How can we analyze sizable codebases to find potential bugs or security flaws and can also offer patches to fix them?
  • Smart debugging. How can we assist developers in locating faults precisely and make suggestions for potential fixes? This can result in considerable time and effort savings for developers and quicker bug response.
  • Continuous integration and deployment. How can we facilitate continuous integration and deployment by identifying code changes that could cause potential conflicts? This will enable developers to resolve issues quickly and roll out production code faster.
  • Predictive maintenance. How can we monitor the performance of the code and find potential issues before they become serious? As a result, developers may proactively address faults, leading to more dependable and stable software.
  • Programming in natural language. How can we allow developers to build code via simple natural-language commands? This can result in more efficient development and a much shorter learning curve for new developers.
  • Test case generation and automation. How can we generate test cases and automate the testing process? In addition to ensuring that software is adequately tested before it is deployed, this can cut down on the time and effort needed for testing.
  • Smart code completion. How can we ensure developers write code faster and more precisely, which completes code snippets based on context? This may lead to fewer mistakes and more effective development.

GitLab’s AI Assisted Code Suggestions are available to select Ultimate customers in a closed beta. For early access consideration, Ultimate customers can submit this form. We’re working towards a wider open beta of this capability in the next few months.

Continue reading our ongoing series, "AI/ML in DevSecOps".

Disclaimer: This blog contains information related to upcoming products, features, and functionality. It is important to note that the information in this blog post is for informational purposes only. Please do not rely on this information for purchasing or planning purposes. As with all projects, the items mentioned in this blog and linked pages are subject to change or delay. The development, release, and timing of any products, features, or functionality remain at the sole discretion of GitLab.

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