19 March 2025
05 Min. Read
Top 5 Alternatives to GitHub Copilot for Software Testing
Looking for more than just GitHub Copilot for your software testing? While Copilot is handy for completing code, other tools offer unique features designed specifically for software testing.
In this blog, we will look at five top alternatives to GitHub Copilot that can make your software testing easier and help you get more done.
What Exactly is GitHub Copilot?
GitHub Copilot is a useful coding tool that simplifies software development. A code editor is included with autocomplete features, providing helpful suggestions that can significantly speed up the coding process.
Created by Microsoft, GitHub, and OpenAI, Copilot employs intelligent algorithms to comprehend your input and provide customized coding solutions. Here is the potential impact it can have on you:
Create boilerplate code: It helps kickstart your projects by generating basic code templates.
Spot bugs and errors: Copilot analyzes your code to find issues, improving overall quality.
Suggest improvements: It offers comments on your code with helpful tips.
Speed up your coding: It provides suggestions to help you complete your code faster.
Real-time help: Copilot gives you instant recommendations, so you don’t get stuck.
Generate documentation: It can create detailed documentation for your projects.
Answer your questions: If you're stuck on something, it can help you find answers.
Fetch relevant info: It pulls up useful information from your codebase.
Why Consider GitHub Copilot Alternatives?
Without being able to detect which code is AI-generated versus human-generated, we have settled for testing our code as much as possible. … It's very much like the times before AI -- engineers are likely copying code from random places on the internet, too. We have to rely on smart folks to read the code, and good unit testing to find the problems.”
-Principal Engineer, Veradigm
While GitHub Copilot offers impressive features like context-aware code suggestions, its capabilities in unit test generation and optimization can be somewhat limited. Businesses may also seek alternatives due to considerations around cost, language support, or the need for integration with specific development stacks.
The Quality of tests from AI can be very questionable
Tests written before code are able to better focus on testing the logic, not the implementation. But tests written after, despite best efforts, are tightly coupled to implementation details that adds wasteful test code making tests longer and more verbose.
AI generated unit tests can slow down releases
When used to write tests after code even AI would have difficulty understanding all code paths or scenarios, producing redundant tests that are difficult to understand, maintain, and collaborate on.
Lack of context can also lead to under-testing that can leave critical parts of the code untested.
AI generated tests can add unnecessary noise in the pipeline
AI generated unit tests do not test code with its dependencies
AI might also not fully understand the intricacies of the programming language, framework, or libraries, leading to tests that are not comprehensive or accurate.
AI generated tests are an overkill for teams that practice TDD
Writing tests after the code builds redundancy by design in AI tests, and this redundancy is hard to remove because it is aiming for completeness. This additional set of tests are an overkill for teams that practice TDD, so the extra coverage has marginal utility.
GitHub Copilot Alternatives in 2025
Here are the top five GitHub Copilot alternatives to be considered in 2025:
✅HyperTest
Using GitHub Copilot for API testing might seem like an easy option, but it has some big drawbacks. Copilot doesn’t fully understand your entire codebase or application, which can lead to tests that aren’t accurate. This can give you false positives and a misleading sense of security about your API’s reliability.
This is where HyperTest comes in. Unlike Copilot, HyperTest understands your real dependencies and how users interact with your application. By taking the actual context into account, it provides more reliable and consistent testing results, ensuring your APIs work as expected in real-world situations.
Learn more about how HyperTest beats GitHub Copilot in testing here:
Key Features:
Comprehensive API Testing – Supports GraphQL, gRPC, and REST APIs.
Asynchronous Flow Testing – Works with Kafka, RabbitMQ, SQS, and more.
Local End-to-End Testing – You can run end-to-end API tests locally before committing your code, which means there is no need to create or manage test environments.
Full Coverage Assurance – Get detailed code coverage reports to catch every edge case.
Seamless CI/CD Integration – Works with Jenkins, CircleCI, GitLab, and others.
Features | Co-pilot | HyperTest |
Reporting and Analytics | It does not provide any reports or analytics. | It offers coverage reports after each test run, along with detailed traces of failing requests across services. |
Performance and Scalability | Its performance depends on the underlying model. | It can test thousands of services simultaneously and runs lightweight tests locally, ensuring high performance. |
Capability | It focuses on code completion and suggestions. | It provides integration testing specifically for developers. |
Testing Focus | It primarily performs unit tests, treating code as the object of testing. | It tests code, APIs, the data layer, inter-service contracts, and queue producers and consumers, focusing on code and dependencies. |
Model of Test Generation | It uses a trained GPT-4 model for generating tests. | It generates tests based on actual user flows or application scenarios. |
Use Case | It tests code in isolation from external components, useful for developers. | It tests code alongside external components, also aimed at developers. |
Failure Types | It identifies logical regressions in the code. | It detects both logical and integration failures in the code. |
Set-up | You install a plugin in your IDE. | You initialize an SDK at the start of your service. |
✅Codeium
Codeium offers AI-powered code suggestions for various programming languages. Whether you are using Python or C++, it helps you build applications quickly and with less unnecessary code.
The autocomplete feature is smart and provides helpful feedback based on what you are working on in real time. You can use Codeium directly from your browser with the Playground feature, or you can install its extension to access its main functions in your preferred IDE.
Features:
Greater Language Support: Codeium supports over 70 programming languages, including some less common ones like COBOL, TeX, and Haskell, unlike GitHub Copilot.
Extensive IDE Support: It works with more than 40 IDEs, allowing you to use its features in your favorite coding environment.
Context Awareness: Codeium analyzes your project files and repository to generate more accurate suggestions.
✅Tabby
Tabby is an open-source AI coding assistant that provides a simple solution for code completion. It gives real-time code suggestions to help developers code faster and with fewer mistakes. If you want an easy-to-use alternative to GitHub Copilot, Tabby is a solid choice.
Tabby works well with VSCode, Atom, and Sublime Text, so you can start using it without changing your editor.
Features:
Offers quick and helpful code completions.
Compatible with various code editors and IDEs.
Available in both free and paid versions.
✅Tabnine
Tabnine operates similarly to Copilot but has some advantages, like personalized AI models, the option for self-hosting, offline access, and code privacy.
The free plan provides basic code completions and suggests code line by line. To get better suggestions from Tabnine, you can give it context using natural language prompts and your own code.
Features:
Extensible: You can connect Tabnine to GPT-3’s codebase to perform more tailored tasks while following specific coding practices and styles.
Customizable: Tabnine offers more support for managing subscriptions and monitoring usage compared to GitHub Copilot.
Switchable Models: You can switch between different large language models (LLMs) in real time while using Tabnine chat for unique responses.
Private Mode: You can deploy Tabnine in secure environments, like on-premises servers, but this is only available in the Enterprise plan.
✅ OpenAI Codex
OpenAI Codex is the AI model that powers GitHub Copilot and can be integrated into your projects. It has been trained on billions of lines of code from public repositories, providing valuable help in software development.
While Codex is mostly trained on Python, it also supports other languages like JavaScript, PHP, Swift, and Ruby.
Features:
Natural Language Prompts: You can interact with OpenAI Codex using text prompts, and it can handle a wide range of tasks.
Customizable: You can integrate Codex into your workflow through an API for direct access to many features, unlike the abstract experience of GitHub Copilot.
Richer Outputs: You receive more detailed responses and outputs since you are interacting directly with the OpenAI Codex model.
Conclusion
While GitHub Copilot can help with creating code, it often misses the bigger picture of your application, making it less reliable for software testing.
The alternatives we have talked about provide better solutions, but HyperTest is a really good alternative because it understands your actual dependencies and how users interact with your app. With HyperTest, you get accurate testing that takes context into account, giving you more confidence in your APIs.
Consider these alternatives, especially HyperTest, to improve your software testing and create strong, high-quality applications!
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