Top 5 AI-Powered VS Code Extensions for Coding & Testing in 2025

One of the software industry’s most important pain points in testing is “Coverage”. It is manually impossible to generate test cases while covering edge cases and different scenarios with the scale of how much code gets written each day. AI Agents here save time and improve test coverage and reliability. Developers can now focus on core logic while AI handles repetitive and error-prone tasks like writing unit tests.

In fact, a managing partner at Y Combinator (YC) recently highlighted that AI agents, AI systems tailored to specific business functions are on the rise. The rise of AI, driven by advances in large language models (LLMs), offers the potential for more efficient, smaller organizations by automating repetitive, administrative tasks.

In this article, we’ll explore a range of VS Code extensions that use AI to help make writing tests seamless and easier.

What Are AI Agents?

AI agents are essentially intelligent systems designed to automate specific business functions or tasks that would typically require human intervention. Think of them as AI-powered "employees" who can perform a wide range of operations, from answering customer queries to managing workflows and even generating tests in one click.

These agents are highly specialized, often built for a particular vertical or domain. AI agents are tailored to automate repetitive, administrative, or time-consuming tasks within a specific industry, making businesses more efficient and reducing the need for large teams.

Characteristics of AI Agents in Development

• Autonomy: Operate without constant human guidance.

• Adaptability: Learn from data and improve over time.

• Interactivity: Engage with users to provide suggestions or perform actions.

• Goal-oriented: Designed to achieve specific objectives, such as code completion or test generation.

How AI Agents Are Transforming Test Generation

AI-powered extensions have become a prime example of how you can automate and speed up integral components of your development process- something like test generation. It may take a lot of context knowledge if you are trying to manually write these test cases. But imagine if an agent could scan through your code in seconds and generate test cases with context for you that too with analyzing code structures, dependencies, and possible edge cases.

Benefits of Using AI Agents for Test Generation

• Time Efficiency: Drastically reduce the time spent on writing tests.

• Improved Test Coverage: Generate comprehensive tests that cover various scenarios.

• Early Bug Detection: Catch potential issues before they progress further in the development cycle.

• Consistency: Maintain a consistent approach to testing across different codebases.

Best AI Code Extensions for VS Code in 2024

Now, let’s explore the top AI-powered extensions that integrate seamlessly with VS Code to enhance your test generation process.

1. Github Copilot

Imagine a coding buddy within your IDE, it writes your code, completes your code, interprets what you want to convey, and also generates the test cases for you. Trained on billions of lines of code, it transforms natural language prompts into actionable code suggestions.

Features

• Contextual Code Suggestions: Provides real-time code suggestions based on the current context.

• Docstring Generation: Automatically generates documentation comments for functions.

• Refactoring Suggestions: Offers recommendations to improve code readability and efficiency.

• Multi-language Support: Works with various programming languages.

Pros

• Increased Productivity: Users report up to 55% faster task completion and enhanced job satisfaction

• Learning Tool: Great for beginners as it suggests best practices and snippets

• Seamless Integration: Directly integrates with Visual Studio Code without disrupting workflow

Cons

• Over-reliance Risk: Developers may become too dependent on suggestions, potentially stunting their coding skills

• Accuracy Issues: Sometimes suggests incorrect or inefficient code

• Licensing Concerns: Potential issues with copyright when suggesting code snippets

2. Tabnine

Tabnine is another great tool in the AI coding assistant arena. It has the ability to generate the test cases for the functions you wish to. It tends to analyze the coding style and practices that are followed by you can generate test cases for your functions based on existing code.

Features

• AI-Powered Code Completions: Offers intelligent code completions based on your coding patterns.

• Team Learning: Learns from team members’ coding styles to provide tailored suggestions.

• Multi-language Support: Compatible with numerous programming languages.

Pros

• Speedy Coding: Significantly reduces the time spent writing repetitive code.

• Customizable AI Models: Allows teams to train models on their specific codebases for better accuracy.

• Integration Options: Works well with various IDEs, including VS Code.

Cons

• Limited Free Version: The free version has limited functionality compared to the paid plans.

• Learning Curve: Some users may find it challenging to configure and optimize its features initially.

• Occasional Irrelevance: Suggestions may not always align perfectly with the developer’s intent.

3. Keploy

Keploy VS Code extension is tailored specifically for testing applications. It allows developers to generate tests automatically in one click based on their existing code and usage patterns, which can drastically reduce the time spent on manual test creation. This extension is particularly useful for teams focused on maintaining high test coverage without the overhead of writing every test case from scratch.

Features

• Automatic Test Generation: Generates tests based on existing code and usage patterns.

• Focus on Test Coverage: Aims to help maintain high test coverage without excessive manual input.

• Validate Testcase: Generated testcases are validated and executed as soon as they are created to check if the are useful or not.

Pros

• Time-Saving Automation: Reduces the effort required to write tests manually, allowing developers to focus on other areas of development.

• Improved Testing Efficiency: Helps ensure that applications are thoroughly tested with minimal overhead.

4. Bito

Bito has been gaining traction as an AI tool that not only helps with coding but also focuses on improving productivity through automation. It can assist in generating tests and documentation, which is a huge plus for maintaining clarity in your codebase.

Features

• Automated Snippet Generation: Quickly generates code snippets and templates for common tasks.

• Documentation Assistance: Helps maintain up-to-date documentation alongside code changes.

• User-Friendly Interface: Designed for easy integration into existing workflows.

Pros

• Productivity Boost: Streamlines repetitive tasks, allowing developers to focus on more complex problems.

• Intuitive Design: Easy to use, making it accessible even for less experienced developers.

• Versatile Functionality: Supports various programming languages and frameworks.

Cons

• Limited Advanced Features: May lack some advanced coding assistance features found in competitors like Copilot or Tabnine.

• Dependency on Internet Connection: Requires an active Internet connection for optimal functionality.

• Performance Variability: Some users report inconsistent performance depending on project complexity.

5. Cursor

Cursor stands out as a VS Code fork that enhances the coding experience significantly. It uses advanced LLM features that are reportedly better than those of GitHub Copilot. One of its coolest features is its predictive capabilities, it can anticipate changes you might want to make in your code, which can save time when writing tests or altering functions

Features

• Predictive Coding Assistance: Anticipates changes and offers suggestions based on user behavior.

• Enhanced LLM Capabilities: Claims to outperform traditional tools in generating accurate code suggestions.

Pros

• Innovative Features: Offers unique predictive capabilities that can save time during coding sessions.

• User-Centric Design: Focuses on improving the overall coding experience rather than just completing tasks.

Cons

• Newer Tool with Growing Pains: As a newer tool, it may have bugs or limitations that are still being addressed.

• Limited Community Support: This may not have as extensive a user community or resources compared to more established tools.

Which AI Code Extension Should You Use?

Choosing the right AI code extension depends on your needs. If you’re looking for general-purpose code completion and suggestions, GitHub Copilot and Tabnine are solid options.

However, if your primary goal is to automate test generation and improve test coverage without manual effort, Keploy stands out as the best choice. Unlike Copilot, which focuses more on writing code, or Tabnine, which learns coding styles, Keploy is designed specifically for easiness of test creation, ensuring your applications are thoroughly tested without extra overhead. With its one-click test generation and automatic validation, Keploy helps teams maintain high test coverage while saving countless hours. For developers prioritizing efficient and automated testing, Keploy is the go-to extension. 🚀

Conclusion

AI-powered VS Code extensions are transforming software testing by automating test generation, improving coverage, and enhancing productivity. As AI agents continue to evolve, they will play an increasingly critical role in software development, allowing developers to focus on innovation while ensuring robust testing practices.

FAQ

Are AI-generated tests compliant with industry standards?

AI-generated tests can follow best practices, but compliance with industry standards depends on the tool’s capabilities and user configurations. It’s always recommended to review and refine the generated test cases

How do AI-powered test generation tools improve software testing?

AI-powered tools enhance test coverage by automatically generating comprehensive test cases, identifying edge cases, and reducing manual effort, allowing developers to focus on critical logic.

Do AI-generated tests integrate with existing testing frameworks?

Yes, most AI-powered test generation tools support popular frameworks like Jest, PyTest, JUnit, and Mocha, making integration seamless within existing workflows.