Test Automation Antipatterns - Examples and How to Avoid Them

Neglecting best practices and falling into test automation anti-patterns can result in code that is not only difficult to follow but also messy and hard to maintain. Just like leaving leftover lasagne in the fridge for too long can lead to a tangled mess, failing to adhere to programming patterns, consistency, and other best practices in test automation can create a codebase that is hard to unravel and manage.

In this post, we will explore certain anti-patterns of writing test frameworks and try to find a way to avoid them.

Example of a Shoddy Test Automation Code:

This is based on a real-life example I have encountered.

Structure:

```python
- piggybacked_desktop_app
- qa_tests
  - tests
    - TestClass1.py
    - TestClass2.py
    - TestClass3.py
    - TestClassN.py
    - TestRunner.py
    - BashTests.py
    - ApiTestClass1.py
    - ApiTestClass2.py
    - ConfigReader.py
  - resources
    - test_data.json
    - config.ini
```

Issues with the test framework structure:

1. Lack of Modularization: The framework's structure lacks clear separation into logical modules, leading to a monolithic and unmaintainable codebase. A modular approach allows for better organization and easier collaboration among team members.

2. Inconsistent Naming Conventions: The file and class naming conventions seem inconsistent and can make it harder to find specific test cases or logic files. Consistent and descriptive naming is crucial for code readability.

3. Overloaded Test Classes: Combining test classes with logic and inline locators results in overloaded classes that serve multiple purposes. This violates the principle of single responsibility and makes the code harder to understand.

4. Poor Test Suite Management: Aggregating test classes in a single array without proper test suite management can lead to inefficient test execution and difficulty in running specific test subsets.

5. Lack of Test Data Management: The framework seems to lack proper test data management, potentially leading to hardcoding data or duplicated test cases.

6. Limited Reusability: The lack of clear separation and modularity can reduce code reusability, making it harder to leverage existing components in new test cases.

To address these issues and create a more maintainable test framework, consider the following improvements:

1. Modularization: Separate logic and test cases into distinct modules, allowing for better organization and easier maintenance. Create separate directories for logic, test cases, and any shared resources.

2. Clear Naming Conventions: Adopt clear and consistent naming conventions for files, classes, and methods to improve code readability and findability.

3. Test Case Management: Implement a proper test case management strategy that allows for easy test case identification and execution.

4. Test Data Management: Use a data-driven approach for test data management, reducing hardcoding and enabling easy updates when data changes.

5. Test Suite Management: Implement a test suite management mechanism that allows for selective test execution and better control over test runs.

6. Separation of Concerns: Ensure that test classes focus solely on test cases and do not contain logic or inline locators. Use page object models or similar design patterns to separate the test logic from the application's internal details.

7. Code Reviews and Refactoring: Conduct regular code reviews to identify areas for improvement and conduct refactoring to eliminate technical debt and improve code quality.

Example of the Test Class Rewritten:

To address the issues in the initial test framework, I have improved the test class structure and organization using the suggested directory structure:

```python
root
   -Config
      -config.ini
   -Model
   -Pages
   -Results
   -tests
       -api
      -web
               -test_class_one.py
               -test_class_two.py
      -conftest.py
   -Utils
```

With the revised directory structure, we have improved the test class overhead by inheriting from the BaseTestClass, which provides common test utilities and setup methods. This reduces repetitive code and promotes code reusability.

test_class_one.py

```python
from BaseTestClass import BaseTestClass
from Pages.LogPage import LogPage

class TestClassOne(BaseTestClass):
        
   def test_something_one(self):
       """TestCaseNumber"""
       self.page = LogPage(self.driver)
       title = self.page.get_page_title()
       assert title == "Page One Title"

   def test_something_two(self):
       """TestCaseNumberTwo"""
       self.page = LogPage(self.driver)
       expected_table_headers = ['header 1', 'header 2']
       actual_table_headers = self.page.get_table_headers()
       assert len(self.page.intersect_lists(expected_table_headers, actual_table_headers) == len(expected_table_headers)
```

In the revised test class, we import the BaseTestClass and LogPage, abstracting the test logic and common methods to the base class. This approach allows for clear separation of test cases, improved modularity, and better organization.

The BaseTestClass provides the necessary test setup, driver initialization, and utility methods like `intersect_lists()` to compare lists. By inheriting from the BaseTestClass, we can reuse these methods and eliminate code duplication.

By following this revised approach, the test framework becomes more maintainable, organized, and scalable. The test cases are now focused solely on their specific test logic, making it easier to understand, modify, and extend the automation suite. Additionally, the utilization of a base class encourages consistency in coding standards and practices throughout the test suite.

Identifying Anti-patterns in Test Automation:

Test automation anti-patterns and their impact on the testing process:

1. Not Adhering to Programming Patterns: Failing to follow well-established programming patterns and practices can lead to code complexity, reduced maintainability, and difficulties in debugging. Lack of structure in the codebase can make it challenging for team members to understand, modify, and extend the automation framework.

2. Lack of Consistency: Inconsistent naming conventions, coding styles, and project structures can hinder collaboration among team members. It may lead to confusion, slower onboarding of new team members, and increased chances of introducing errors due to misunderstandings.

3. Not Using Existing Libraries and Frameworks: Reinventing the wheel by building functionalities already available in well-established libraries or frameworks can result in wasted time and effort. Utilizing existing resources can streamline development, improve code quality, and leverage community support for issue resolution.

4. Brittle and Flaky Tests: Writing tests that are overly sensitive to minor changes in the application can result in frequent test failures, impacting team confidence in test results. Brittle tests may require constant maintenance and can slow down the testing process.

5. Hard-Coding Test Data: Embedding test data directly into test scripts makes it difficult to maintain and update data. Using external data sources, such as test data files or databases, allows for easier management and updating of test data.

6. No Clear Test Scopes: Failing to define clear test scopes can lead to confusion regarding what tests cover and what remains untested. It may result in overlooking critical aspects of the application and missing potential defects.

7. Ignoring Test Dependencies: Tests with dependencies on external resources, such as databases or APIs, may produce inconsistent results due to data changes or environmental variations. Neglecting to handle test dependencies appropriately can lead to unreliable test outcomes.

8. Unrealistic Test Expectations: Setting overly strict pass/fail criteria or unrealistic expectations for test results can lead to a false sense of security. Tests may pass, yet critical defects might still exist due to the narrow focus of the test cases.

9. Test Data Pollution: Tests that leave behind test data or artifacts can impact the stability of subsequent test runs. Failure to clean up after test execution may result in data conflicts or false positives/negatives in test results.

10. Ignoring Test Maintenance: Neglecting regular test maintenance can result in obsolete tests that no longer accurately reflect application behavior. Unmaintained tests can lead to false positives or false negatives, reducing trust in the testing process.

11. Minimal Error Reporting: Insufficient error reporting in test failures can hinder defect investigation and resolution. Detailed error messages and logs are crucial for identifying the root cause of test failures.

12. Poorly Designed Test Data Generation: Generating test data without considering boundary cases or edge scenarios can lead to incomplete test coverage. Well-designed test data generation should encompass a wide range of scenarios to ensure comprehensive testing.

13. Neglecting Test Environment Management: Failure to manage test environments effectively can lead to inconsistent test results due to differences in configurations or setups. Test environments should be maintained to mimic production as closely as possible.

14. Lack of Collaboration and Communication: Isolating test automation efforts from the rest of the development process can result in misaligned objectives and redundant work. Effective collaboration and communication between testers, developers, and other stakeholders are essential for successful test automation.

15. Overlooking Accessibility Testing: Neglecting to include accessibility testing in the automation strategy can lead to applications that are not accessible to users with disabilities. Accessibility should be considered an integral part of the testing process.

By avoiding these test automation anti-patterns, teams can improve the overall quality and efficiency of the testing process. Emphasizing best practices, maintaining code cleanliness, and leveraging existing resources can lead to more reliable, maintainable, and effective test automation suites.

Recognizing when a test automation framework might be heading in the wrong direction

It's essential to prevent wasting resources and ensure that your testing efforts remain effective and efficient. Here are some warning signs that your test automation framework might be going off track:

1. Complexity and Unmanageable Codebase: If the codebase of your test automation framework becomes overly complex, difficult to navigate, and hard to maintain, it's a clear indication that the framework might be heading in the wrong direction. Complicated code can lead to increased technical debt, making it challenging to add new test cases or make updates.

2. Low Test Stability and Flakiness: When test cases frequently fail for no apparent reason or exhibit inconsistent results, it indicates that the test automation framework might not be reliable. Test flakiness can be caused by issues like timing problems, poor synchronization, or unclear test steps.

3. Slow Test Execution: If the test automation framework takes a significant amount of time to execute test cases, it can impact the productivity of the testing team and slow down the development process. Slow test execution can occur due to inefficient code, lack of parallel execution, or excessive dependencies.

4. Lack of Reusability: When test cases are not designed with reusability in mind, you might end up duplicating test code, leading to maintenance challenges. A good test automation framework should promote reusable components and modules to maximize efficiency.

5. Difficulty in Adding New Test Cases: If it becomes difficult and time-consuming to add new test cases to the framework, it suggests that the framework lacks flexibility and adaptability. The framework should allow for easy integration of new test cases and testing scenarios.

6. Inadequate Test Coverage: If the test automation framework fails to cover critical aspects of the application or misses essential test scenarios, it indicates a potential flaw in the testing strategy. Test coverage should be comprehensive and align with the project's testing goals.

7. Lack of Reporting and Analysis: When the test automation framework lacks proper reporting and analysis capabilities, it becomes challenging to identify test failures, track progress, and make data-driven decisions. Effective reporting is crucial for assessing test results and identifying areas of improvement.

8. High Maintenance Overhead: If maintaining the test automation framework becomes a time-consuming and resource-intensive task, it might indicate that the framework is not designed for scalability and ease of maintenance.

9. Inconsistent Test Design and Coding Standards: When test cases within the framework are designed and coded with different styles and standards, it can lead to confusion and inconsistency. A unified and standardized approach should be followed for test design and coding.

10. Limited Test Framework Documentation: If the test automation framework lacks proper documentation and guidelines for test case creation, test data management, and test environment setup, it can hinder collaboration and knowledge sharing among team members.

To address these warning signs and steer your test automation framework in the right direction, consider conducting regular code reviews, refactoring code to improve maintainability, investing in proper training for the testing team, and leveraging established testing frameworks and best practices. Regularly evaluate the framework's performance, stability, and coverage to ensure it aligns with the project's testing objectives.

How to handle such a test framework:

It all depends on the timing and resources your team has (or you alone). An option that will probably be least costly in resources is to analyze the codebase and then perform a sanitization of the test framework by starting with organizing code into separate classes:

```python
- qa_tests
  - tests
    - TestClass1.py
    - TestClass2.py
    - TestClass3.py
    - TestClassN.py
    - TestRunner.py
    - BashTests.py
    - ApiTestClass1.py
    - ApiTestClass2.py
    - ConfigReader.py
  - page_objects
    - Page1.py
    - Page2.py
  - resources
    - test_data.json
  - util
    - json_reader.py
    - config_reader.py
    - report_writer.py
  - reporting
    - screenshots
  - config.ini
```

A more time and resource-consuming option is to redo the whole test framework based on an existing library like unittest or pytest in this case. Using this approach, you can benefit from the following improvements:

- Test Organization: Both unittest and pytest allow you to organize test cases into test classes or modules, making it easier to manage and maintain your test suite.

- Test Discovery: With unittest and pytest, test discovery is automated, meaning you don't need to manually specify which tests to run. The testing frameworks will automatically find and execute all test cases.

- Test Fixtures: Unittest and pytest offer fixture support, which allows you to set up preconditions and clean up after test cases, ensuring consistent test environments.

- Test Reporting: These testing frameworks provide built-in test reporting, generating clear and concise test reports that help identify test failures and their causes.

- Assertions: Both frameworks offer a rich set of assertion methods, making it easy to check expected outcomes and verify test results.

- Plugins and Extensions: pytest, in particular, has a wide range of plugins and extensions that can enhance testing capabilities and integrate with other tools.

- Community Support: Both unittest and pytest are widely used testing frameworks, so you'll find extensive community support, documentation, and resources to help you improve your testing process.

- Compatibility: Since these frameworks are widely used, they are compatible with various test runners and Continuous Integration (CI) tools, making it easier to integrate testing into your development workflow.

Example of the Test Class Rewritten:

To address the issues in the initial test framework, we have improved the test class structure and organization using the suggested directory structure:

```python
root
   -Config
      -config.ini
   -Model
   -Pages
   -Results
   -tests
       -api
      -web
               -test_class_one.py
               -test_class_two.py
      -conftest.py
   -Utils
```

With the revised directory structure, we have improved the test class overhead by inheriting from the BaseTestClass, which provides common test utilities and setup methods. This reduces repetitive code and promotes code reusability.

test_class_one.py

```python
from BaseTestClass import BaseTestClass
from Pages.LogPage import LogPage

class TestClassOne(BaseTestClass):
        
   def test_something_one(self):
       """TestCaseNumber"""
       self.page = LogPage(self.driver)
       title = self.page.get_page_title()
       assert title == "Page One Title"

   def test_something_two(self):
       """TestCaseNumberTwo"""
       self.page = LogPage(self.driver)
       expected_table_headers = ['header 1', 'header 2']
       actual_table_headers = self.page.get_table_headers()
       assert len(self.page.intersect_lists(expected_table_headers, actual_table_headers)) == len(expected_table_headers)
```

In the revised test class, we import the BaseTestClass and LogPage, abstracting the test logic and common methods to the base class. This approach allows for clear separation of test cases, improved modularity, and better organization.

The BaseTestClass provides the necessary test setup, driver initialization, and utility methods like `intersect_lists()` to compare lists. By inheriting from the BaseTestClass, we can reuse these methods and eliminate code duplication.

By following this revised approach, the test framework becomes more maintainable, organized, and scalable. The test cases are now focused solely on their specific test logic, making it easier to understand, modify, and extend the automation suite. Additionally, the utilization of a base class encourages consistency in coding standards and practices throughout the test suite.

Conclusion:

By addressing test automation anti-patterns and implementing best practices, teams can improve the quality, efficiency, and reliability of their test automation efforts. Emphasizing modular and organized code structures, consistency, and reusability can lead to more robust, maintainable, and effective test automation suites, ultimately contributing to the success of your testing process.

Happy Testing!