15 AI Tools to help you Code Bug-Free in 2023

AI code tools have become a popular choice for developers in 2023, with various tools available that leverage artificial intelligence to assist in generating high-quality code. 

One of the most notable tools is OpenAI Codex, which can translate natural language to code and powers GitHub Copilot. OpenAI Codex can interpret simple commands and applies to almost any programming task, making it a versatile developer tool. 

Another tool that has garnered attention is AlphaCode by DeepMind, which has been shown to outperform human coders in certain situations. Other AI code tools are available, each with its strengths and weaknesses.

Despite the benefits of these tools, it's important to note that they are not yet completely safe to use. Researchers have found that engineers who use AI tools are more likely to introduce security vulnerabilities to their apps. 

This can lead to various problems, from minor inconveniences to major security breaches.

Artificial intelligence (AI) has emerged as a powerful tool for finding and fixing software bugs in recent years. 

AI-powered tools can scan code much faster than humans and identify patterns humans might miss.

As AI technology develops, we expect to see more powerful and effective AI tools for finding and fixing software bugs. Here are 15 AI tools that are available today:

• Google AI Test Kitchen: Google AI Test Kitchen is a platform for developing and testing AI-powered applications. It provides a variety of tools and resources to help developers create and test AI applications that are reliable and bug-free.

• DeepMind's AlphaCode: DeepMind's AlphaCode is a large language model that can generate code. It can generate code that is of high quality and bug-free.

• Microsoft's CodeX: Microsoft's CodeX is a large language model that can generate code. It can generate code that is of high quality and bug-free.

• Facebook's AI Research Lab: Facebook's AI Research Lab is developing a variety of AI tools that can be used to find and fix bugs in software. These tools include:

• • BugSeer: BugSeer is a tool that uses machine learning to find bugs in code.
  • BugSpotter: BugSpotter is a tool that uses machine learning to find bugs in text.
  • BugSniffer: BugSniffer is a tool that uses machine learning to find image bugs.

• Plaid AI: Plaid AI is a company that develops AI tools for finding and fixing bugs in software. Their tools include:

• • Plaid Detect: Plaid Detect is a tool that uses machine learning to find bugs in code.
  • Plaid Fix: Plaid Fix is a tool that uses machine learning to fix bugs in code.

• Snyk: Snyk is a company that develops AI tools for finding and fixing security vulnerabilities in software. Their tools include:

• • Snyk Code: Snyk Code is a tool that uses machine learning to find security vulnerabilities in code.
  • Snyk Container: Snyk Container is a machine-learning tool to find security vulnerabilities in container images.
  • Snyk Infrastructure: Snyk Infrastructure is a tool that uses machine learning to find security vulnerabilities in infrastructure as code.

• SonarSource: SonarSource is a company that develops AI tools for finding and fixing bugs and security vulnerabilities in software. Their tools include:

• • SonarLint: SonarLint is a tool that integrates with IDEs to find bugs and security vulnerabilities in code.
  • SonarCloud: SonarCloud is a cloud-based platform that provides various tools for finding and fixing bugs and security vulnerabilities in software.

• Checkmarx: Checkmarx is a company that develops AI tools for finding and fixing security vulnerabilities in software. Their tools include:

• • Checkmarx CxSAST: Checkmarx CxSAST is a static analysis tool that uses machine learning to find security vulnerabilities in code.
  • Checkmarx CxIAST: Checkmarx CxIAST is an interactive analysis tool that uses machine learning to find security vulnerabilities in code.
  • Checkmarx CxSCA: Checkmarx CxSCA is a software composition analysis tool that uses machine learning to find security vulnerabilities in third-party components.

• IBM Security AppScan: IBM Security AppScan is a tool suite that uses machine learning to find security vulnerabilities in software. These tools include:

• • IBM Security AppScan Source: IBM Security AppScan Source is a static analysis tool that uses machine learning to find security vulnerabilities in code.
  • IBM Security AppScan Standard: IBM Security AppScan Standard is an interactive analysis tool that uses machine learning to find security vulnerabilities in code.
  • IBM Security AppScan Enterprise: IBM Security AppScan Enterprise is a tool suite that uses machine learning to find security vulnerabilities in software.

These are just a few of the many AI tools available to help developers find and fix bugs in software. 

As AI technology develops, we can expect to see more powerful and effective tools available. 

How to integrate AI code assistance into VScode

There are several AI code assistance extensions available for VScode. Some popular options include:

• GitHub Copilot: GitHub Copilot is an AI-powered code completion tool that can help you write code faster and more accurately.

• Tabnine: Tabnine is another AI-powered code completion tool that can help you write code faster and more accurately.

• AISense: AISense is an AI-powered code assistant that can help you with various tasks, such as code completion, linting, and refactoring.

In this tutorial we will be using Github Copilot. 

GitHub Copilot is easy and more user-friendly; the code rarely breaks when working.

 

Install the AI code assistance extension: Open your Vscode, navigate to the extension panel by the left and search for Copilot.

 

Configure the Copilot assistance extension. Once installed, you must configure it. This typically involves setting up your coding preferences and providing the extension with access to your code repositories.

Start using the Copilot assistance extension. Once configured, you can use it. To do this, start typing code in VScode, and Copilot will suggest completions and other helpful information.

Here are some additional tips for using AI code assistance in VScode:

• Use the AI code assistance extension as a starting point. The AI code assistance extension is a powerful tool, not a replacement for human judgment.Always use your judgment when using the AI code assistance extension, and do not be afraid to change the code that the AI suggests.

• Experiment with different AI code assistance extensions. There are several different AI code assistance extensions available for VScode. Experiment with different extensions to find one that works best for you.

• Provide feedback to the AI code assistance extension developers. The AI code assistance extension developers are constantly working to improve their products. Provide feedback to the developers so that they can make the AI code assistance extensions even better.

Conclusion 

As AI technology develops, we can expect to see even more powerful and effective AI tools become available. This will help developers create more reliable and secure software, benefiting everyone.

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