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How to Write Maintainable Test Scripts: Tips and Tricks

During the development phase, the application is not stable due to frequent deployments from the development team. When automation scripts are created based on this development environment, though there will be updates on functionality, but still we can reduce maintenance efforts in many other ways. In this discussion, we will discuss some good best practices to follow that help in reducing the maintenance of automation code.

Use Descriptive Names

While creating test scripts, it’s always good to ensure that you provide clear and descriptive names for the tests and the variables that we define in the function. This helps for easy understanding for everyone in the team while revisiting the scripts at a later stage.

For example, A function that tests user login functionality:

Bad name: test1()
Good name: test_login_with_valid_credentials()

Providing descriptive names is vital for other reasons, too, like:

  • Reduced Maintenance Time: You won’t need to spend time interpreting complex names when modifying or debugging tests.
  • Enhanced Collaboration: In automation, multiple team members will create test scripts. So to ensure collaboration and avoid duplicates, it’s always preferred to give descriptive names for the functions. With descriptive names, it will be easier for the team members to understand each function’s purpose and usage.

Tips for Descriptive Names

  • Include Functionality: Briefly mention the functionality being tested (e.g., “UserLogin,” “ProductSearch”).
  • Specify Data: If relevant, incorporate data used in the test (e.g., “UserLogin_ValidCredentials,” “ProductSearch_EmptyQuery”).
  • Indicate Expected Outcome: Mention the expected result (e.g., “Success,” “ExpectedBehavior”).
  • Maintain Consistency: Follow a naming convention to ensure clarity and ease of understanding throughout your test suite.

Keep Tests Simple and Focused

Another good practice is to check each test should check only one scenario or functionality. If you add multiple scenarios in a single function, the test becomes more complex and difficult to debug. Also, it will be very difficult to understand where the failure happened.

We can connect many reasons for keeping the test simple, such as:

  • Reduced Complexity: Simpler scripts are easier to understand, debug, and maintain.
  • Improved Reliability: Smaller, focused tests are less susceptible to errors and provide clearer results.
  • Enhanced Reusability: Simpler tests can be easily combined or modified for various testing scenarios.

Tips for Simple Tests

  • Test One Thing at a Time: While creating or reviewing the script, ensure each function verifies only one behavior or scenario and then optimize the script.
  • Break Down Complex Scenarios: It’s always a good practice to break down complex test cases into smaller, more manageable steps.
  • Utilize Descriptive Names: Clear names make the script simpler and convey the purpose of that script.
  • Avoid Unnecessary Logic: Keep script logic focused on the specific functionality being tested.

Implement Comments and Documentation

It’s always a best practice to add comments for any functions or validations that we create in the framework. You may have written a function, and later, for maintenance, when you revisit, it will be difficult to understand the function or what each validation is up to. If any other team member is debugging, they may get confused with the logic if no proper comments are added. The below points also explain why comments are crucial:

  • Improved Maintainability: Detailed comments explain the purpose of specific code sections, simplifying maintenance and modification of tests.
  • Effective Collaboration: Clear documentation facilitates knowledge transfer between testers and developers, promoting efficient teamwork.
  • Reduced Debugging Time: Comments can act as breadcrumbs, guiding you through the thought process behind the script logic and aiding in faster debugging.
Let’s see an example of how we can add a comment to a function:
def test_invalid_login_attempts_are_restricted():
  # This test verifies that a user is locked out after three failed login attempts
  setup_user_account("test_user")
  for _ in range(3):
  attempt_login("test_user", "wrong_password")
  assert is_user_locked("test_user"), "User should be locked out after three failed attempts"

Structure and Organize

Structuring and organizing your test scripts plays a critical role in maintaining a clean, efficient, and manageable testing environment. Use a logical structure that mirrors your application’s architecture.

For example, we can keep a good directory structure based on the app components like:
/tests
  — /authentication
    — — test_login.py
    — — test_logout.py
  — /shopping_cart
    — — test_add_item.py
    — — test_remove_item.py

Requirement of Structure

  • Improved Maintainability: Organized test suites are easier to navigate, update, and expand upon.
  • Enhanced Reusability: Well-structured tests can be easily reused across different testing scenarios.
  • Reduced Duplication: Clear organization minimizes the risk of creating redundant test cases.
  • Efficient Execution: Organized test suites facilitate faster test execution and reporting.

Tips for Organized Structure

  • Grouping by Functionality: Organize tests based on the functionalities they validate (e.g., Login, Product Search, Checkout).
  • Modularization: Break down complex test suites into smaller, modular components focusing on specific features or modules within the application.
  • Utilize Test Folders: Leverage folders within your testing framework to categorize and group related tests.
  • Naming Conventions: Apply consistent naming conventions for test scripts and folders to promote clarity and searchability.
  • Prioritization: Prioritize your tests based on criticality (e.g., smoke tests, regression tests) to ensure thorough yet efficient execution.
  • Dependency Management: Identify and document dependencies between tests to ensure proper execution order.

Use Test Data Effectively

Test data is one of the most important parts of test automation, so you should be careful when handling this and use separate test data for every scenario so there won’t be any false positive bugs when running the test cases in parallel. Read more about Parallel Testing.

For example, suppose we have multiple scenarios that perform different combinations of payment methods, and these scenarios are running in parallel. In that case, we should ensure all these scenarios use different credentials; otherwise, the test cases will fail as they all use the same login.

We can keep test data in JSON, CSV, XML, or any other format. Based on the scenario, we can call the test data, like below is the JSON file containing multiple credentials:
{
  "user1": {
    "username": "testuser1",
    "password": "securepassword1"
  }
  
  "user2": {
    "username": "testuser2",
    "password": "securepassword2"
  }
}
In the script, we can call test data as below:
import json

def load_test_data(file_path):
  with open(file_path, 'r') as file:
    return json.load(file)

def test_login():
  data = load_test_data("test_data.json")
  assert login(data["user1"]["username"], data["user1"]["password"])

Test data is also crucial due to below reasons:

  • Improved Test Coverage: Diverse test data allows you to explore a wider range of scenarios, enhancing your tests’ overall coverage and effectiveness.
  • Reduced Risk of Defects: Feeding your tests with realistic and varied data increases the likelihood of uncovering potential bugs or edge cases.
  • Enhanced Reusability: Separating test data from script logic allows you to reuse the same script with different data sets for various testing needs.
  • Simplified Maintenance: Managing test data efficiently reduces the workload associated with modifying or updating your test scripts.

Tips for Effective Test Data Usage

  • Identify Data Requirements: Carefully analyze the functionalities being tested to determine the types of data needed (e.g., valid usernames, invalid passwords, product details).
  • Choose Data Sources: Select appropriate locations to store your test data, such as external files (CSV, Excel), databases, or even code repositories.
  • Data-Driven Testing: Utilize data-driven testing techniques to separate test data from script logic, enabling you to easily modify data sets without altering the core script.
  • Data Validation: Implement mechanisms to validate the accuracy and integrity of your test data, ensuring it reflects realistic scenarios.
  • Data Maintenance: Regularly review and update your test data as your application evolves or new functionalities are introduced.

Use Stable Element Locators

Using stable element locators ensures your tests function consistently and avoid unexpected failures due to minor changes in the page’s structure. Test scripts relying on unstable locators are vulnerable to breaking with minor changes in the application’s underlying code (HTML structure).

Here’s why using stable element locators is crucial:

  • Reduced Test Flakes: Stable locators minimize the risk of tests failing due to minor UI changes, leading to more reliable and trustworthy test results.
  • Improved Maintainability: Scripts using stable locators are easier to maintain as they are less likely to require updates when the application’s UI evolves.
  • Enhanced Readability: Clear and consistent locators improve the readability of your test scripts, making them easier to understand for everyone.

Tips for Stable Element Locators

  • Prioritize IDs: If available, utilize unique IDs assigned to elements as they provide the most reliable way to locate them.
  • Leverage Names: When IDs are unavailable, opt for unique element names within your test’s context.
  • Utilize CSS Selectors: Construct well-defined CSS selectors that target specific attributes and their values in a unique combination.
  • XPaths as a Last Resort: Use XPaths cautiously, as they can be brittle and susceptible to changes in the HTML structure. When necessary, prioritize shorter and more focused XPaths targeting specific element relationships.

Employ Assertions Wisely

Assertions are crucial as they help understand where the test case failed. So, it’s always good practice to add more assertions. Effective use of assertions ensures your scripts accurately validate whether the application functions as intended. Read about assertion testing here.

Here’s why test assertions is crucial:

  • Confirmation of Expected Behavior: Assertions verify if the application’s behavior aligns with your expectations based on the test scenario.
  • Enhanced Debugging: Well-placed assertions provide valuable information during debugging, pinpointing where the application might deviate from the expected outcome.
  • Improved Readability: Assertions enhance the readability of your scripts by clearly stating the expected behavior.
  • Automated Reporting: Many testing frameworks integrate seamlessly with assertions, facilitating automatic test reporting and failure identification. Read: Test Reports – Everything You Need to Get Started.
Let’s see an example of adding an assertion for an email updation scenario:
def test_user_profile_update():
  original_profile = get_user_profile("test_user")
  update_user_profile("test_user", new_email="[email protected]")
  updated_profile = get_user_profile("test_user")
  assert updated_profile["email"] == "[email protected]", "Email should be updated in the user profile"

Tips for Assertions

  • Focus on Critical Verifications: Prioritize assertions that validate core functionalities and expected outcomes.
  • Clarity and Simplicity: Keep assertions clear and concise, focusing on verifying the specific behavior.
  • Error Messages: Utilize informative error messages within your assertions to aid debugging efforts.
  • Avoid Redundancy: Avoid excessive assertions that clutter your scripts and provide diminishing returns.
  • Negative Assertions: Consider using negative assertions to verify the absence of unexpected behavior.

Adopt a Coding Standard

Adopting coding standards helps increase test scripts’ readability and maintainability. Several established coding standards exist for popular testing frameworks and programming languages.

Here are some examples:

  • Java: Google Java Style Guide, Oracle Code Conventions for Java
  • Python: PEP 8 – Style Guide for Python Code
  • JavaScript: Airbnb JavaScript Style Guide, StandardJS

There are many reasons for adopting a coding standards:

  • Improved Readability: Consistent formatting, naming conventions, and commenting styles enhance the readability of your scripts for everyone involved in the testing process.
  • Reduced Maintenance Time: Adherence to a standard simplifies script maintenance by promoting a common understanding of the code structure.
  • Enhanced Collaboration: A shared coding standard facilitates teamwork by ensuring everyone writes scripts consistently and understandably.
  • Reduced Errors: Following established conventions minimizes the risk of introducing errors due to inconsistencies in script formatting or logic inconsistencies.

Tips for Implementing Coding Standards

  • Choose a Standard: Select a well-established coding standard that aligns with your chosen scripting language (e.g., PEP 8 for Python, Google JavaScript Style Guide).
  • Document Your Standard: Create a clear and concise document outlining the specific coding conventions and practices expected for your test scripts.
  • Integrate with Tools: Utilize code formatters or linters to enforce the coding standard within your test scripts, automatically identifying and correcting formatting or style violations.
  • Educate and Train: Ensure your team knows the coding standards and implementation guidelines.

Read the best practices to maintain coding standards in test scripts.

Use Version Control

Version control systems (VCS) act like a sophisticated time machine for your automation test scripts, allowing you to track changes, revert to previous states, and collaborate seamlessly with your team members. By employing VCS, you safeguard the history and evolution of your test scripts, ensuring their continued growth and stability.

Here’s why using version control is crucial for your test scripts:

  • Change Tracking: VCS meticulously tracks every modification made to your scripts, enabling you to revert to previous versions if necessary.
  • Collaboration: Version control facilitates teamwork by allowing multiple testers to work on scripts simultaneously without conflicts.
  • Version History: You can easily access past versions of your scripts to understand how they have evolved.
  • Improved Debugging: Version control helps pinpoint the exact change that introduced a bug, streamlining the debugging process.

Below are a few popular Version Control Systems for Test Scripts:

  • Git: A widely used distributed VCS offering robust features for version tracking, branching, and merging.
  • Subversion (SVN): A centralized VCS that is relatively simpler to learn but offers less flexibility compared to Git.

Tips for Version Control

  • Choose a VCS: Select a version control system that aligns with your team’s preferences and familiarity.
  • Integrate with Your Workflow: Utilize features offered by your testing framework or IDE to integrate version control with your testing process seamlessly.
  • Commit Regularly: Make frequent commits to the version control system with clear and descriptive commit messages.
  • Utilize Branching: Leverage branching functionalities to isolate changes and work on new features without affecting the main codebase.
  • Resolve Conflicts: Version control systems can help identify and resolve conflicts that might arise when multiple testers modify the same script concurrently.

Plan for Test Maintenance

Maintaining test scripts is an ongoing process. To ensure their effectiveness, allocate regular time to update and improve your test suite. This includes periodic refactoring to incorporate improvements and reduce redundancy.

Here’s why planning for test maintenance is crucial:

  • Reduced Regression Risks: Regular maintenance minimizes the likelihood of regressions (unexpected issues introduced by code changes) slipping through the testing process.
  • Improved Efficiency: A well-maintained test suite executes efficiently, minimizing wasted time and resources during testing cycles.
  • Enhanced Test Coverage: Proactive maintenance ensures your tests cover the application’s evolving functionalities.
  • Simplified Collaboration: A clear maintenance plan fosters communication and collaboration within your testing team.

Tips for Test Maintenance

  • Schedule Regular Reviews: Establish a defined schedule for reviewing test scripts, identifying outdated or redundant tests, and updating them based on application changes. Prioritize maintenance efforts on critical tests that cover core functionalities.
  • Integrate with CI/CD Pipeline: Incorporate test maintenance activities into your CI/CD pipeline to automate script updates and regression testing after code changes.
  • Monitor Test Results: Regularly analyze test results to identify trends and potential areas for improvement within your test suite.
  • Document Maintenance Activities: Maintain clear documentation outlining changes made to your test scripts and the rationale behind them.

Transforming Testing with Codeless Automation

While we’ve explored numerous best practices for crafting maintainable test scripts, even the most well-written ones require some upkeep. This becomes especially true as the number of automated test cases grows, leading to potentially larger test repositories and increased debugging time.

This challenge has driven the industry towards codeless automation tools. These tools offer an alternative approach, allowing testers to create automated tests without writing traditional code. This capability can significantly reduce maintenance overhead, particularly for large test suites.

While many tools claim to be codeless automation tools, their functionality doesn’t come close to what they offer in their marketing materials. testRigor stands out from these and is a generative AI-based codeless automation tool, which lets you write test scripts in plain English, thus saves you enormous maintenance time and effort. Learn in detail about codeless test automation.

testRigor

testRigor is an AI-driven codeless automation tool with many incredible and useful features. Let’s go through a few features relevant to test script maintenance.

  • Generative AI: testRigor can generate test cases or test data based on the test description we provide. It makes the testers’ lives extremely easy by saving a lot of time and effort for them.
  • Free from programming languages: While using testRigor, we don’t have to worry about knowledge of programming language. Yes, testRigor helps create test scripts in parsed plain English. So, this advantage helps manual testers immensely, that is why it is the automation testing tool for manual testers. They can create and execute test scripts three times faster than other tools. Also, any stakeholder can add or update test scripts, as they are easy to read and understand.
  • Say goodbye to Flaky XPaths: testRigor doesn’t rely on flaky XPaths or DOM element properties; it has a different element locator strategy. You can just mention the text that you see on the screen for the element; testrigor’s AI algorithms will be able to fetch the element and continue automation. So we don’t have to worry about the development/element property changes happening in the application.
Let’s review a sample test script in testRigor, which gives more clarity about the simplicity of the test cases:
login as customer
click "Accounts"
click "Manage Accounts."
click "Enable International Transactions"
enter stored value "daily limit value" into "Daily Limit"
click "Save"
click "Account Balance" roughly to the left of "Debit Cards"
check the page contains "Account Balance"

As you can see, no complicated XPath/CSS locator is mentioned, and no complex loops or scripts are required. Apart from the simplistic test case design and execution, some advanced features help you test your application using simple English commands.

  • Reusable Rules (Subroutines): You can easily create functions for the test steps that you use repeatedly. You can use the Reusable Rules to create such functions and call them in test cases by simply writing their names. See the example of Reusable Rules.
  • Global Variables and Data Sets: You can import data from external files or create your own global variables and data sets in testRigor to use them in data-driven testing.
  • 2FA, QR Code, and Captcha Resolution: testRigor efficiently manages the 2FA, QR Code, and Captcha resolution through its simple English commands.
  • Email, Phone Call, and SMS Testing: Use simple English commands to test the email, phone calls, and SMS. These commands help validate 2FA scenarios, with OTPs and authentication codes being sent via email, phone calls, or via phone text.
  • File Upload/ Download Testing: Execute the test steps involving file download or file upload without the requirement of any third-party software. You can also validate the contents of the files using testRigor’s simple English commands.
  • Database Testing: Execute database queries and validate the results fetched.

testRigor enables you to test web, mobile (hybrid, native), API, and desktop apps with minimum effort and maintenance. You can go through all the features of testRigor here.

Conclusion

The goal of using automation is to reduce the effort of the testing team in executing frequently executed test scripts manually. So, with automation, the QA team can focus more on testing edge scenarios. However, achieving this goal using traditional coded automation tools is a bit challenging as the team needs to spend more effort on script maintenance, which impacts the overall quality of the product and delays in product releases.

Hence, the better option is to shift towards AI-powered tools like testRigor. These tools reduce the tester’s effort in script creation and maintenance, leading to faster product releases and potentially improved product quality.

FAQs

What is the difference between codeless automation and coded automation?

Codeless and coded automation are two approaches to test automation. Coded automation requires testers to write scripts in a programming language, demanding technical expertise. In contrast, codeless automation uses a visual interface or pre-built components, allowing testers to create tests without coding. Codeless tools are easier to use and maintain, making them ideal for teams without extensive programming experience. Here is a blog to calculate the ROI of codeless automation tools.

Is it good to have an automation tool that supports multiple types of automation?

Having one automation tool that supports multiple types (API, mobile, web, desktop) offers several benefits. Firstly, it simplifies your test suite by using a familiar interface and reducing the need to learn and manage multiple tools. This streamlines the testing process and improves team efficiency.

Additionally, a single tool can promote consistency in your testing approach across different platforms, leading to more robust test coverage. Finally, it can minimize licensing costs by consolidating your testing needs into one platform.

Join the next wave of functional testing now.
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