AI-powered code completion tools are becoming increasingly popular among software developers as they provide a more efficient way to create and maintain software. Code completion tools are used to reduce the amount of code that needs to be written and improve the accuracy of the code written. These tools along with a host of DevSecOps automation services help DevOps teams streamline their workflows by automating certain development and deployment tasks. For example, AI-Powered Code Completion tools can autocomplete code for developers, which can help speed up the development process, reduce errors, and ultimately improve the quality of the code.

To successfully implement AI-powered code completion tools, organizations must have a DevSecOps strategy in place. This means integrating security into the entire software development lifecycle, including during code writing. There are many DevSecOps tools available that can help organizations achieve this goal. These tools can be used to automate security testing and ensure that code is written securely from the beginning of the development process. This blog will explore the types, examples, and benefits of AI-powered code completion tools.  

What are AI-powered Code Completion Tools?  

AI-powered code completion tools use artificial intelligence techniques to help programmers write code more quickly and efficiently. These tools analyze the code the programmer is writing and suggest how to complete it based on patterns and best practices observed from a large corpus of code. 

Traditionally, programmers write code manually by typing out each line, which can be time-consuming. However, with AI-powered code completion tools, much of the coding can be automated. The tool predicts what the programmer is trying to accomplish and generates code snippets that can be inserted with just a few keystrokes. 

AI-powered code completion tools typically use a combination of machine learning algorithms, natural language processing techniques, and other AI techniques to analyze the code the programmer is writing and any relevant data sources such as APIs or databases. The tool then generates suggestions for completing the code, such as suggesting the names of variables or functions or generating code snippets that can be inserted into the code. 

Types of AI-powered code completion tools 

There are several types of AI-powered code completion tools available today, including: 

Text-based code completion tools 

These tools use natural language processing (NLP) algorithms to analyze code and suggest completions based on the context of the code. They can also use machine learning (ML) algorithms to learn from the code developers write over time and provide more accurate suggestions. 

Visual code completion tools 

These tools provide suggestions in a graphical format, such as a list of available functions or code snippets that can be dragged and dropped into the code editor. 

Integrated development environment (IDE) code completion tools 

These tools are built into popular IDEs like Visual Studio, Eclipse, and JetBrains and provide context-aware code suggestions while developers are typing. 

Cloud-based code completion tools 

These tools are hosted in the cloud and can be accessed through a web browser or an API. They can analyze code written in different programming languages and suggest completions based on the code’s context. 

Code generation tools 

These tools use AI to automatically generate code snippets based on developers’ input. They can be used to create boilerplate code or to automate repetitive coding tasks. 

AI-powered code completion tools are becoming increasingly sophisticated and can help developers work faster and more efficiently. As AI technology continues to evolve, we can expect even more advanced and specialized code-completion tools in the future.  

Popular AI-Powered Code Completion Tools  

  • Amazon CodeWhisperer – integrates with AWS’s existing development tools, including AWS CodeCommit, AWS CodeBuild, and AWS CodePipeline, and can be used with third-party code editors and IDEs. It is designed to help teams streamline development processes, reduce the risk of errors, and improve overall code quality. 
  • GitHub Copilot – a tool based on OpenAI’s GPT (Generative Pre-trained Transformer) language model, which is capable of generating high-quality natural language text uses machine learning algorithms to analyze the code that a developer is working on and then suggests code snippets or complete functions that match the context of the code being written. 
  • TabNine – a code completion tool powered by AI and machine learning models that can suggest entire lines of code based on the context of what you’re typing 
  • Kite – an AI-powered autocomplete tool that uses deep learning to suggest code completions for various programming languages. 
  • Codota – an AI-driven code completion tool that can provide intelligent suggestions based on millions of open-source code snippets and usage patterns. 
  • IntelliCode – a set of AI-powered tools built into Microsoft Visual Studio that can provide intelligent code completion, suggestions, and other productivity features 
  • DeepTabNine – a free and open-source AI-based code completion tool that uses deep learning algorithms to suggest code completions and improve developer productivity 
  • Eclipse – is based on machine learning algorithms that use AI to analyze the code and provide suggestions for completing it. The tool uses various techniques, such as natural language processing, deep learning, and neural networks, to provide accurate and relevant suggestions to the developer. 

Benefits of using AI-powered Code Completion Tools  

AI-powered code completion tools are an essential component of the modern DevSecOps environment. They offer a range of benefits that can improve the efficiency, accuracy, and quality of the software development process, ultimately leading to better products and happier customers. 

Increased efficiency 

AI-powered code completion tools can help developers write code more quickly and efficiently. They automate the tedious task of writing and debugging code, allowing developers to focus on the more creative and complex aspects of software development. 

Improved code quality 

By suggesting best practices and identifying potential bugs and errors, AI-powered code completion tools can help improve the overall code quality. It not only makes the code more reliable and secure but also saves time and effort by reducing the need for manual debugging. 

Reduced errors 

Mistakes can occur during coding due to a lack of attention or experience, but AI-powered code completion tools can help mitigate these errors. They offer contextually relevant suggestions and eliminate the possibility of human error, leading to more accurate and precise code. 

Enhanced collaboration 

AI-powered code completion tools can facilitate collaboration by providing suggestions that adhere to best practices and conventions. It can help reduce conflicts between team members with different coding styles, leading to a more cohesive and efficient development process. 

Increased development speed 

 By streamlining the coding process, AI-powered code completion tools can help speed up the development process, which is especially important in fast-paced environments like DevOps. This can lead to faster releases and more frequent updates. 

Cost savings 

Using AI-powered code completion tools can reduce the need for manual coding, which can help save time and reduce costs associated with the development process. It can also help reduce the need for additional staff, freeing up resources for other aspects of the development process. 


AI-powered code completion tools are transforming the way DevSecOps teams work. These tools can help increase productivity, reduce errors, and speed up software development cycles by providing developers with intelligent and automated code suggestions. From auto-completing syntax to suggesting code snippets, these AI tools can perform complex tasks and reduce developers’ workload. However, it is advisable not to see the tools as a replacement for human developers but rather as helpful tools that can assist them in their work. As technology evolves, we can expect to see even more sophisticated AI-powered code completion tools to make software development faster and more efficient.