| title | seoTitle | seoDescription | datePublished | cuid | slug | canonical | cover | tags |
|---|---|---|---|---|---|---|---|---|
Exploring Unit Test Generative Tools |
Top Tools for Generating Unit Tests Automatically |
Discover top unit test generative tools to boost code quality, save time, and improve test coverage in your development workflow. |
Wed Apr 09 2025 06:17:42 GMT+0000 (Coordinated Universal Time) |
cm99jf2eh000609jredfdfncz |
exploring-unit-test-generative-tools |
unit-testing, test-automation |
Introduction:
Artificial Intelligence (AI) has revolutionized various industries, including software development. One particular area where AI has shown significant promise is generating unit tests effortlessly. With the help of AI-based tools, developers can automate the process of creating unit tests, saving time and effort. In this blog, we will delve into the pros and cons of AI-generated unit tests, highlighting their potential benefits and addressing any concerns.
If you're a software newbie, you may have heard the phrase "unit test" used quite a lot. But what is a unit test, and why do we care?
Simply put, a unit test is a form of software test that targets verifying individual parts—or "units"—of your code to ensure they behave as expected. Such units are often the smallest units of testability, like functions, methods, or classes.
Unit tests enable developers to catch bugs early in the development process. Rather than waiting until a feature is finished—or worse, until it's live—unit tests enable you to test every little piece of your codebase as you write it. That way, you can catch and resolve issues immediately before they become larger issues.
For instance, if you have a function that adds two numbers, a unit test for that function would check that add(2, 3) should return 5. If you then modify the logic within the function and it becomes broken, the test will fail—informing you at once that something went awry.
Pros of AI-Generated Unit Tests:
-
Increased Code Coverage:
AI-powered tools can automatically create test cases that cover a wide range of scenarios in an application. These unit tests help reveal corner cases and edge scenarios that developers might not have considered beforehand. Consequently, this leads to better code coverage, ensuring that potential issues are identified and fixed. -
Reduced Manual Effort:
Generating comprehensive unit tests manually can be a time-consuming task. AI-based tools automate this process, significantly reducing the effort required from developers. By saving resources, developers can focus on other critical tasks, such as feature development or fixing complex bugs. -
Faster Feedback Loop:
AI-generated unit tests can be executed quickly and frequently, allowing developers to receive immediate feedback on code changes. This accelerated feedback loop facilitates faster bug detection, enabling swift iterations and ensuring higher-quality code. -
Improved Test Quality:
AI-generated unit tests, often based on sophisticated algorithms, can be more robust and thorough compared to manually written tests. They can examine various aspects of software behavior and generate test cases that check complex interactions between components. By identifying potential bugs or edge cases, AI tests contribute to enhanced software quality.
Cons of AI-Generated Unit Tests:
-
Lack of Human Intuition:
AI-generated tests might overlook certain scenarios that humans would have caught. Human testers can use their creativity and intuition to identify potential issues that AI algorithms might not consider. Relying solely on AI-generated unit tests can result in missing out on specific test cases. -
Difficulty with Ambiguous Requirements or Complex Systems:
AI tools operate based on pre-defined rules and patterns. They can struggle when faced with ambiguous or changing requirements or complex software systems with intricate dependencies. Writing unit tests that tackle such scenarios and adapt to changing needs often requires human intervention. -
Inability to Handle Non-Functional Requirements:
Unit tests generated by AI primarily focus on functional aspects and may not cover non-functional requirements such as performance, security, or usability. Identifying issues related to these constraints often lies beyond the scope of AI-generated tests. -
Learning Curve and Tool Limitations:
Implementing AI-generated unit tests typically requires developers to familiarize themselves with new tools and techniques. This learning curve might slow down the initial adoption of AI-based testing. Moreover, the availability and quality of AI tools can vary, and not all programming languages or frameworks may be supported equally.
Unit testing is an essential software development practice that guarantees individual program components work properly. In software testing, when you define unit testing in software testing, it means the process of testing and isolating the smallest program code units, i.e., functions or methods, to ensure their proper behavior. Through this practice, developers are able to identify bugs early and have high-quality codes. Unit tests are normally automated and developed in parallel with the application code. With unit testing, teams can refactor and build out their software safely with no risk.
Current LLM based tools in the market -
With the rise of complex and large-scale software applications, the need for efficient and effective testing tools has become of paramount importance. Enter AI generative Language Model (LLM) based testing tools, designed to generate test cases, automate testing processes, and improve overall software quality. In this blog post, we will explore two popular AI generative LLM-based testing tools currently available in the market: Codium, Copilot test and Keploy.
Both Codium and Copilot are examples of the potential AI generative LLM-based tools hold for software development and testing. By leveraging the power of language models, these tools can automate manual processes, improve productivity, and enhance the quality of software applications. But drawback of these tools is they don't take user command as input which can result in flaky and false positive tests.
Keploy.io Demo
The plugin is available in newer versions of IntelliJ (Community/Ultimate).
Go to VsCode -> Extensions -> Keploy, and search for “keploy”. Enable the extension, and you’re ready to go!
After successful installation, you’ll see "Welcome to Keploy" page. Here you can select whether you want to generate Unit tests or Integration Tests.
Primarily Keploy focuses on the integration tests which mainly includes record/replay modes. As described below -
Record Mode- It's used to capture the real network traffic coming to the application to create test cases and mocks.
Test Mode - In this the test case captured are converted into HTTP requests and bombarded back to application but instead of making actual calls to the external services or DB it uses mock data instead.
So here as well you can choose integration test for that, but if you are. interested in writing Unit tests that are not flaky. You can choose "Generate Unit test" option below. Then it will lead to a page where the instructions to follow are there. Basically you just need to click on the widget coming above each function. And all set !!
![](https://cdn.hashnode.com/res/hashnode/image/upload/v1725018845808/e5040e75-e635-4770-9dbc-b960f2cb8e6d.png align="center")
You will see the following logs, which will indicate the unit test generation has successfully started. So keploy has itself detected the language and will create a test file. for the particular source file i.e the file which is currently opened. Also it will be running the test commands which are popular for that language like - npm test which will internally use jest framework. Maven test for java which will be using Jacoco framework. and similarly go test and Py test for Go and Python respectively.
So as you can see below keploy is communicating with openAI to get the generated tests but instead of just blindly writing multiple tests and moving on. It is making those tests run 5 times which will ensure the tests are not flaky and adding the coverage as well. If any test fail to increase the current coverage. Keploy will itself remove those tests. Isn't it deduplication along with test generation ? By default keploy will be running 10 iterations i.e it will make request to the openAI model 10 times for the test results.
▓██▓▄
▓▓▓▓██▓█▓▄
████████▓▒
▀▓▓███▄ ▄▄ ▄ ▌
▄▌▌▓▓████▄ ██ ▓█▀ ▄▌▀▄ ▓▓▌▄ ▓█ ▄▌▓▓▌▄ ▌▌ ▓
▓█████████▌▓▓ ██▓█▄ ▓█▄▓▓ ▐█▌ ██ ▓█ █▌ ██ █▌ █▓
▓▓▓▓▀▀▀▀▓▓▓▓▓▓▌ ██ █▓ ▓▌▄▄ ▐█▓▄▓█▀ █▓█ ▀█▄▄█▀ █▓█
▓▌ ▐█▌ █▌
▓
version: 2.3.0-beta14
🐰 Keploy: 2024-08-30T17:53:41+05:30 INFO Generating tests for file: ./src/routes/routes.js
🐰 Keploy: 2024-08-30T17:53:41+05:30 INFO Running test command to generate coverage report: 'npm test -- --coverage --coverageReporters=text --coverageReporters=cobertura --coverageDirectory=./coverage'
Getting indentation for new Tests...
Streaming results from LLM model...
Getting Line number for new Tests...
Streaming results from LLM model...
Current Coverage: 77.000000% for file "./src/routes/routes.js"
Desired Coverage: 100.000000% for file "./src/routes/routes.js"
Generating Tests...
Streaming results from LLM model...
🐰 Keploy: 2024-08-30T17:53:56+05:30 INFO Total token used count for LLM model gpt-4o: 1866
🐰 Keploy: 2024-08-30T17:53:56+05:30 INFO Validating new generated tests one by one
🐰 Keploy: 2024-08-30T17:53:56+05:30 INFO Running test 5 times for proper validation with the following command: 'npm test -- --coverage --coverageReporters=text --coverageReporters=cobertura --coverageDirectory=./coverage'
🐰 Keploy: 2024-08-30T17:53:56+05:30 INFO Iteration no: 1
🐰 Keploy: 2024-08-30T17:53:57+05:30 INFO Iteration no: 2
🐰 Keploy: 2024-08-30T17:53:58+05:30 INFO Iteration no: 3
🐰 Keploy: 2024-08-30T17:53:59+05:30 INFO Iteration no: 4
🐰 Keploy: 2024-08-30T17:54:00+05:30 INFO Iteration no: 5
🐰 Keploy: 2024-08-30T17:54:01+05:30 INFO Skipping a generated test that failed to increase coverage
🐰 Keploy: 2024-08-30T17:54:01+05:30 INFO Running test 5 times for proper validation with the following command: 'npm test -- --coverage --coverageReporters=text --coverageReporters=cobertura --coverageDirectory=./coverage'
🐰 Keploy: 2024-08-30T17:54:01+05:30 INFO Iteration no: 1
🐰 Keploy: 2024-08-30T17:54:02+05:30 INFO Iteration no: 2
🐰 Keploy: 2024-08-30T17:54:03+05:30 INFO Iteration no: 3
🐰 Keploy: 2024-08-30T17:54:04+05:30 INFO Iteration no: 4
🐰 Keploy: 2024-08-30T17:54:05+05:30 INFO Iteration no: 5
🐰 Keploy: 2024-08-30T17:54:06+05:30 INFO Generated test passed and increased coverage
🐰 Keploy: 2024-08-30T17:54:06+05:30 INFO Running test 5 times for proper validation with the following command: 'npm test -- --coverage --coverageReporters=text --coverageReporters=cobertura --coverageDirectory=./coverage'
🐰 Keploy: 2024-08-30T17:54:06+05:30 INFO Iteration no: 1
🐰 Keploy: 2024-08-30T17:54:08+05:30 INFO Test failed in 1 iteration
🐰 Keploy: 2024-08-30T17:54:08+05:30 INFO Skipping a generated test that failed
🐰 Keploy: 2024-08-30T17:54:08+05:30 INFO Running test 5 times for proper validation with the following command: 'npm test -- --coverage --coverageReporters=text --coverageReporters=cobertura --coverageDirectory=./coverage'
🐰 Keploy: 2024-08-30T17:54:08+05:30 INFO Iteration no: 1
🐰 Keploy: 2024-08-30T17:54:09+05:30 INFO Test failed in 1 iteration
🐰 Keploy: 2024-08-30T17:54:09+05:30 INFO Skipping a generated test that failed
🐰 Keploy: 2024-08-30T17:54:09+05:30 INFO Running test command to generate coverage report: 'npm test -- --coverage --coverageReporters=text --coverageReporters=cobertura --coverageDirectory=./coverage'
Current Coverage: 79.000000% for file "./src/routes/routes.js"
Desired Coverage: 100.000000% for file "./src/routes/routes.js"
Generating Tests...
Streaming results from LLM model...
🐰 Keploy: 2024-08-30T17:54:19+05:30 INFO Total token used count for LLM model gpt-4o: 1955
🐰 Keploy: 2024-08-30T17:54:19+05:30 INFO Validating new generated tests one by one
🐰 Keploy: 2024-08-30T17:54:19+05:30 INFO Running test 5 times for proper validation with the following command: 'npm test -- --coverage --coverageReporters=text --coverageReporters=cobertura --coverageDirectory=./coverage'
🐰 Keploy: 2024-08-30T17:54:19+05:30 INFO Iteration no: 1
🐰 Keploy: 2024-08-30T17:54:20+05:30 INFO Iteration no: 2
🐰 Keploy: 2024-08-30T17:54:21+05:30 INFO Iteration no: 3
🐰 Keploy: 2024-08-30T17:54:22+05:30 INFO Iteration no: 4
🐰 Keploy: 2024-08-30T17:54:23+05:30 INFO Iteration no: 5
🐰 Keploy: 2024-08-30T17:54:24+05:30 INFO Skipping a generated test that failed to increase coverage
🐰 Keploy: 2024-08-30T17:54:24+05:30 INFO Running test 5 times for proper validation with the following command: 'npm test -- --coverage --coverageReporters=text --coverageReporters=cobertura --coverageDirectory=./coverage'
🐰 Keploy: 2024-08-30T17:54:24+05:30 INFO Iteration no: 1
🐰 Keploy: 2024-08-30T17:54:25+05:30 INFO Iteration no: 2
🐰 Keploy: 2024-08-30T17:54:26+05:30 INFO Iteration no: 3
🐰 Keploy: 2024-08-30T17:54:27+05:30 INFO Iteration no: 4
🐰 Keploy: 2024-08-30T17:54:28+05:30 INFO Iteration no: 5
🐰 Keploy: 2024-08-30T17:54:29+05:30 INFO Generated test passed and increased coverage
🐰 Keploy: 2024-08-30T17:54:29+05:30 INFO Running test 5 times for proper validation with the following command: 'npm test -- --coverage --coverageReporters=text --coverageReporters=cobertura --coverageDirectory=./coverage'
🐰 Keploy: 2024-08-30T17:54:29+05:30 INFO Iteration no: 1
🐰 Keploy: 2024-08-30T17:54:30+05:30 INFO Test failed in 1 iteration
🐰 Keploy: 2024-08-30T17:54:30+05:30 INFO Skipping a generated test that failed
🐰 Keploy: 2024-08-30T17:54:30+05:30 INFO Running test 5 times for proper validation with the following command: 'npm test -- --coverage --coverageReporters=text --coverageReporters=cobertura --coverageDirectory=./coverage'
🐰 Keploy: 2024-08-30T17:54:30+05:30 INFO Iteration no: 1
🐰 Keploy: 2024-08-30T17:56:26+05:30 INFO Test failed in 1 iteration
🐰 Keploy: 2024-08-30T17:56:26+05:30 INFO Skipping a generated test that failed
🐰 Keploy: 2024-08-30T17:56:26+05:30 INFO Running test command to generate coverage report: 'npm test -- --coverage --coverageReporters=text --coverageReporters=cobertura --coverageDirectory=./coverage'
Current Coverage: 81.000000% for file "./src/routes/routes.js"
Desired Coverage: 100.000000% for file "./src/routes/routes.js"So unlike other tools like codiumate, Keploy tries to automate the process by himself using the configurations to make developers life easier.
![](https://cdn-images-1.readmedium.com/v2/resize:fit:800/1*IjuIhyA4LIEuNm7nSFAF1w.gif align="left")
Conclusion:
AI-generated unit tests offer a range of advantages, from increased code coverage to reduced manual effort and faster feedback loops. By automating test case generation, developers can enhance software quality and productivity. However, it is crucial to acknowledge the limitations. Human intuition, handling of complex scenarios, and non-functional requirements cannot be overlooked. As AI continues to evolve, striking a balance between AI-generated and manually written tests will be imperative to develop robust and high-quality applications.
The primary reason for unit testing is to ensure that individual pieces of code behave as desired.
It traps bugs early in the development cycle before hitting production.
Unit testing supports code reliability and simpler debugging.
It also leads developers to code more clean and modular code.
Unit testing targets testing individual functions or methods independently.
Integration testing verifies how various units interact as a team.
Unit tests are quicker and easier to execute regularly during development.
Integration tests are more general and typically executed later in the test cycle.
Not all functions require a unit test, but complex and critical logic must be tested.
Testing simple code may not be worth the time unless it impacts significant features.
Prioritize areas with high business value or those that are changed often.
Finding a balance prevents test coverage from hindering development.
Make tests small, targeted, and simple to comprehend.
Use clear naming to describe what each test verifies.
Avoid external dependencies like databases or APIs—mock them if needed.
Run your tests regularly to catch issues as early as possible.