Software testing is the foundation of reliable software delivery. However, understanding the terminology can be tricky, especially for beginners. Terms like unit testing and automated testing are often used interchangeably, leading to confusion. The truth is that unit testing and automated testing are not competitors—they complement each other.
By the end of this post, you’ll know:
- What unit testing and automated testing mean individually
- The key differences between the two
- How they interact to form a robust testing strategy
- Why do software teams need both
What is Unit testing?
Unit testing is software testing that focuses on checking the smallest pieces of code – called units. Units usually refer to individual functions or methods.
The purpose? To verify that each unit performs exactly as intended, in complete isolation. This means no dependencies on databases, APIs, or other systems. Unit tests are typically automated and written by developers during the coding process.
Who performs Unit testing?
Developers primarily perform unit testing as an integral part of the software development process, rather than by dedicated quality assurance (QA) testers. This proactive approach allows developers to write and execute unit tests alongside their code, ensuring each unit functions correctly in isolation.
What are the main components of Unit testing?
The main components of a unit test include:
- Test cases, which are specific scenarios designed to validate the functionality of the code
- Assertions, which are statements that check whether the actual outcomes match the expected results, and
- Test suite, which is a collection of related test cases that can be executed together.
Unit testing typically occurs during the coding phase of the software development lifecycle. It is often associated with test-driven development (TDD), a methodology where tests are written before the actual code implementation.
Unit testing example
Let’s consider a banking application where we want to verify the behavior of a function that determines whether a user is eligible for a loan. The function calculates eligibility based on the user’s income and existing loans.
Here is the function in Python:
We can write a unit test to validate the function:
In this example, the unit test verifies various inputs and their expected outputs. If the function does not behave as expected, the test will fail, and immediate feedback will be provided to the developer.
Why is Unit testing important?
- Fast execution: Unit tests are lightweight and execute quickly.
- Immediate feedback: Developers know instantly if a function breaks.
- Confidence to refactor: Developers can change code without fear of breaking existing functionality.
- Cost-effective: Unit tests don’t require complex setups.
Unit testing ensures that the building blocks of your application are solid, creating a strong foundation for more complex tests.
What are the best practices for integrating Unit Testing into the development process?
Integrating unit testing into the development process is essential for ensuring code quality and reliability. Here are some best practices to effectively incorporate unit testing:
1. Write readable and simple tests
Unit tests should be easy to read and understand. Using a clear structure, such as the Arrange-Act-Assert (AAA) pattern, helps maintain simplicity and clarity in test cases. This approach allows developers to quickly identify what the test is doing, making it easier to maintain and refactor over time.
2. Automate Unit tests
Setting up an automated testing process that runs tests regularly—such as daily or with each code commit—ensures that any issues are identified early in the development cycle. This integration into a continuous integration/continuous deployment (CI/CD) pipeline allows all team members to access and review test reports, fostering collaboration and discussion around code quality metrics.
3. Isolate tests
Each unit test should focus on a single unit of functionality without relying on external systems or other tests. This isolation can be achieved using test doubles, such as mocks or stubs, which simulate the behavior of external dependencies. Isolated tests are faster, more reliable, and easier to diagnose when they fail.
4. Avoid test interdependence
Unit tests should be independent to prevent a single failure from cascading through the entire test suite. By ensuring that each test can run in isolation, developers can more easily pinpoint issues and maintain a stable testing environment.
5. Combine Unit and Integration testing
While unit tests validate individual components, integration tests assess how these components work together. Following the testing pyramid model, unit tests should form the base of your testing strategy, with integration tests used sparingly to validate interactions between components.
6. Ensure repeatability and scalability
Unit tests should be repeatable under various conditions and scalable as the application grows. Writing tests alongside application code—especially in methodologies like Test-Driven Development (TDD)—ensures that appropriate tests accompany every new feature or bug fix.
7. Use appropriate tools
Leverage frameworks suited for your programming language to streamline the unit testing process. Popular frameworks include JUnit for Java, NUnit for .NET, and pytest for Python, which provide essential tools for writing, running, and reporting on tests.
8. Focus on critical features
While achieving high test coverage is important, prioritize writing meaningful tests that cover critical functionalities rather than aiming for 100% coverage with low-quality tests.
9. Run tests frequently
Integrate unit testing into your daily development routine by running tests frequently—ideally before merging code changes into the main branch—to catch regressions early.
10. Conduct peer reviews
Encourage peer reviews of code and test cases to identify edge cases and enhance overall test quality.
What is Automated testing?
While unit testing focuses on individual units of code, automated testing is a broader concept that involves automating the execution of test cases using tools, frameworks, or scripts.
Automated testing aims to replace manual testing efforts with automated processes that are faster, repeatable, and less error-prone.
Automated testing covers various types of tests, including:
- Unit tests: Validate individual functions or methods.
- Integration tests: Verify that different components or systems work together correctly.
- End-to-end (E2E) tests: Simulate real user workflows to ensure the application behaves as expected.
- Performance tests: Measure how well the system performs under load or stress.
- Regression tests: Confirm that new changes do not break existing functionality.
Automated testing example
Let’s build on the banking app example. Imagine we must test the complete user workflow for transferring funds between two accounts.
End-to-end (E2E) test:
Using Selenium, we can automate the entire user process:
This automated E2E test simulates a real user workflow, from logging in to transferring funds and verifying the updated balance. Unlike unit tests, E2E tests involve multiple components, such as the user interface, database, and backend logic.
Unit testing vs Automated testing: Key differences
While unit testing is a subset of automated testing, there are important differences between the two:
Why you need both Unit tests and Automated tests
Although unit tests provide a strong foundation, they are not sufficient on their own. Unit tests verify that individual functions work but cannot ensure the system behaves correctly.
This is where other automated tests—such as integration and E2E tests—become essential.
Together, unit tests and automated tests create a robust safety net that:
- Catches bugs at both the micro and macro levels.
- Provides fast feedback during development.
- Simulates real-world scenarios to validate system behavior.
For example:
- Unit Tests ensure that the calculateTransferFee() function works as expected.
- Integration Tests verify that transferring funds updates both accounts in the database.
- End-to-end tests simulate a user logging in, performing a transfer, and checking their balance.
Combining these tests ensures that individual components and the entire application function reliably.
Tools for Unit testing and Automated testing
To implement unit testing and automated testing effectively, developers and testers use a variety of tools:
Popular Unit Testing Tools
- JUnit: For Java applications.
- pytest: A flexible testing framework for Python.
- NUnit: For .NET-based testing.
- Jest: A JavaScript framework commonly used for React applications.
Popular Automated Testing Tools
- Selenium: Automates browser-based end-to-end testing.
- Cypress: Simplifies front-end testing.
- JMeter: Measures system performance under load.
- Postman: Automates API testing.
- Appium: Tests mobile applications on both Android and iOS
Key metrics to evaluate Unit tests and Automated testing
Metrics provide teams with quantifiable insights into their testing efforts, helping them identify gaps, assess test performance, and ensure reliable code delivery:
1. Code coverage
A high code coverage percentage indicates that most codebase has been tested. This metric highlights untested areas, allowing teams to address potential blind spots in their test suites.
Why it matters:
- Code coverage ensures that critical parts of the code are validated during tests.
- It provides a clear view of test completeness across the codebase.
While a high coverage percentage is desirable, it does not guarantee the quality of tests. For example, executing code without verifying meaningful outputs can create a false sense of security. Achieving 100% coverage is often impractical and does not necessarily indicate bug-free software.
2. Test pass rate
Consistently high pass rates indicate stable and reliable code, while frequent test failures may signal new defects, unstable features, or inadequate test coverage.
Why it matters:
- A declining pass rate highlights emerging issues that require immediate attention.
- Monitoring trends over time helps identify problematic code changes or fragile tests.
If a new deployment reduces the test pass rate from 98% to 85%, this suggests recent changes introduced bugs or conflicts. Investigating and addressing the failures quickly will prevent defects from reaching production.
3. Defect density
A lower defect density typically reflects higher code quality and more effective testing practices. Teams use this metric to assess whether their tests uncover enough issues relative to code growth.
Why it matters:
- Defect density provides an objective measure of overall code quality.
- It helps teams identify areas of the codebase that require additional testing or refactoring.
If defect density increases despite good coverage, it may indicate that tests are missing edge cases or complex scenarios. Addressing this can improve the effectiveness of both unit and automated tests.
4. Test execution time
Tests that run quickly deliver faster feedback to developers, which is especially important in continuous integration (CI) pipelines. Slow tests can delay deployments and reduce productivity.
Why it matters:
- Faster execution times enable more frequent test runs, improving defect detection.
- Identifying slow-running tests allows teams to optimize performance and prioritize critical tests.
5. Test stability and flakiness
A high rate of flaky tests undermines trust in the testing process, as failures may not indicate real defects.
Why it matters:
- Stable tests provide reliable feedback on code health.
- Flaky tests consume time and resources and lead to confusion during debugging.
6. Defect detection effectiveness
This metric helps teams evaluate how effectively their testing processes prevent bugs from reaching production.
Why it matters:
- Higher defect detection rates during testing reduce costly post-release fixes.
- It reflects the overall quality of the test suite and the team’s ability to validate the software before deployment.
The testing process can be effective if 90% of defects are caught during testing and only 10% surface after release. On the other hand, frequent post-release bugs indicate gaps in the test suite or inadequate coverage of critical paths.
7. Test case effectiveness
Well-designed tests should validate meaningful scenarios and catch potential errors. Tests that rarely fail or do not identify issues may need to be reviewed or improved.
Why it matters:
- Effective tests focus on critical areas and uncover real problems.
- Monitoring test effectiveness ensures that the test suite evolves alongside the codebase.
How Global App Testing bridges the automation gap
At Global App Testing, our flexible crowdtesting solution enhances your automated testing processes, enabling you to make progress where teams traditionally struggle. Here’s how:
1. Instant scalability
Our global network of human testers can execute manual test cases across real devices, physical environments, and geographies – areas that automated scripts often miss. By scaling your manual testing efforts, we allow your team to focus on improving automation coverage.
2. Support for in-house teams
We work alongside your QA and QE teams, providing reliable test execution that addresses immediate gaps in coverage. This approach reduces bottlenecks and creates the time needed to prioritize automation.
3. Execution of automation-queued test cases
Automation queues often become backlogged, particularly when flaky tests or infrastructure challenges arise. Offloading these test cases to our testers ensures critical tests continue running while your engineers address automation setbacks.
What else do we offer
Beyond supporting your test automation efforts, GAT offers a full suite of testing services to enhance software quality at every stage:
- Functional testing: Validate that your product works as expected.
- User & UX testing: Gain deeper insights into how users interact with your software.
- Globalization testing: Combine functional, user, and localization tests to drive international growth.
- Accessibility testing: Ensure your product is compliant with WCAG standards and accessible to all users.
- Exploratory testing: Discover unexpected issues through structured yet flexible testing approaches.
- Regression testing: Safeguard against bugs in existing workflows after updates.
- Device compatibility testing: Confirm functionality across browsers, operating systems, and IoT devices.
- Localization and translation testing: Validate localized content to ensure natural and meaningful experiences for global users.
- Performance and release testing: Ensure your product is stable, fast, and ready for deployment.
Are you curious to see how much of your test suite we can help you automate? Let’s schedule a call to explore the possibilities together.
Keep learning
Functional vs Regression Testing - What's the Difference?
Functional vs Non-Functional Testing - What's the Difference?
What is Test Data Management? - The Definitive Guide