Automated Functional Testing: Key Concepts and Best Practices

Automated functional testing is a software testing method in which specialized tools automatically test scripts. The test scripts verify that an application’s functionalities perform as expected, simulating user interactions and validating outputs against predefined criteria. This ensures that software behaves correctly and has consistent and repeatable test results under various conditions. Automated functional testing ultimately improves software quality and delivery speed.

This article explores automated functional testing, how it works, and its key benefits. We also explore practical use cases and how they can improve software quality and development efficiency.

Summary of Key Automated Functional Testing Concepts

The rest of the article explores these concepts in detail.

Automated functional testing overview

Automated functional testing uses specialized tools to execute test cases and verify that an application's features work as intended. User interactions are simulated, and outcome results are checked against expected outcomes. Teams can ensure the application performs correctly under various conditions, leading to higher software quality and reliability.

Automated testing tools can run tests simultaneously across different environments and configurations. Tests are run quickly and repeatedly, saving time and effort compared to manual methods. This is especially beneficial for ongoing regression testing. Teams can focus on more complex and strategic aspects of software development over testing.

Automated approaches are designed to run test cases similarly during each run, allowing for validating critical application functionalities. This minimizes human error and ensures tests run consistently and results remain reliable in every execution—inconsistencies such as missed testing steps or incorrect data entry are prevented.

Automation enables extensive testing across various scenarios and large data sets. You can efficiently handle various inputs, outputs, and conditions. Broader coverage helps identify edge cases and rare scenarios that might go unnoticed in manual testing.

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Test case design

When designing test cases, engineers identify detailed scenarios describing specific interactions and expected outcomes to validate application functionality. Standard best practices include:

• Establishing clear naming conventions.
• Prioritizing test cases based on their importance.
• Determining the run frequency of your test suit.

Effective test case design ensures that critical functionalities are tested and that tests can be easily understood and maintained over time.

import unittest

class LoginTests(unittest.TestCase):

    def setUp(self):
        # Set up the test environment (e.g., WebDriver, database connection)
        self.driver = webdriver.Chrome()
        self.driver.get("http://example.com/login")

    def test_login_valid_user(self):
        # Test valid user login
        username = self.driver.find_element(By.NAME, "username")
        password = self.driver.find_element(By.NAME, "password")
        username.send_keys("validuser")
        password.send_keys("validpassword")
        password.send_keys(Keys.RETURN)
        self.assertIn("Welcome", self.driver.page_source)

    def test_login_invalid_user(self):
        # Test invalid user login
        username = self.driver.find_element(By.NAME, "username")
        password = self.driver.find_element(By.NAME, "password")
        username.send_keys("invaliduser")
        password.send_keys("wrongpassword")
        password.send_keys(Keys.RETURN)
        self.assertIn("Invalid credentials", self.driver.page_source)

    def tearDown(self):
        # Clean up (e.g., close WebDriver)
        self.driver.quit()

if __name__ == "__main__":
    unittest.main()

Test case design with Selenium WebDriver

Test environment setup

Automated testing requires isolated and consistent test environments for accurate and reliable results. Configuring hardware, software, and networks ensures that testing conditions closely resemble the live environment. The goal is to reduce the risk of environment-related issues during production.

Test data management

Test data management involves handling and organizing the data used in testing. You create, maintain, and secure test data to ensure reliability and repeatability. Proper test data management increases test accuracy and avoids result inconsistencies.

To create a test data management strategy, start with some of the following questions:

• Legal considerations: Legal compliances, such as HIPPAA or GDPR, may determine how test data should be generated.
• Resource constraints: If test environment data is generated at the same scale as a production environment, costs and server memory can be issues. Consider tradeoffs between simulation accuracy and real-world restraints.
• Data copying: Is data copied from production directly to testing environments? While many companies successfully use this approach, monitoring continuously for security risks that could lead to data leakage is important.
• Diversity of data: Would increased data diversity lead to improved testing outcomes? What potential edge cases exist in the data that more robust test data generation strategies could catch?

Test data management strategies usually require coordination between technical and non-technical personnel. When done well, they can improve test suite speed and quality.

Result analysis and reporting

Interpret test outcomes and generate meaningful reports. For example, you highlight risk areas, identify defect patterns, and provide other actionable insights for improving the application. Detailed and accurate reporting helps stakeholders understand application health and make informed decisions about future development.

Some metrics to monitor include:

Test coverage percentage

“Test coverage” is the percentage of the application’s functionalities covered by automated tests. High test coverage indicates that most of the app has been tested.

Increase test coverage percentage by writing unit tests that cover more pieces of functionality, especially in critical data flows. Test for behavior—ensure that tests cover different paths a user may take to the same in-app destination. For example, don’t test login flows using the same user credentials and configuration for every test. Test users with different email signups, OAuth logins, and single sign-on (SSO) providers. Even if some details must be mocked, this pays off when engineers are immediately alerted that an OAuth provider has become temporarily unavailable, for instance. Automation allows this to happen.

Defect detection rate

Another way to think of this metric is “how often faulty code is accurately detected”. This metric helps teams understand how well the tests identify issues and whether improvements are needed.

A low defect detection rate could be the result of things. Over-reliance on timing-based tests is a common offender. Benchmarks and performance checks in unit tests should be flexible enough to allow for small spikes in network connectivity and other factors. Avoid writing test code prone to race conditions where possible, and consider mocking unstable production components in testing environments.

Test execution time

Monitor the time it takes to run automated tests to assess efficiency and identify potential bottlenecks. This dramatically affects engineers’ ability to iterate on code quickly. If executing a full test suite takes several hours, pushing a bug fix to production could take hours or even days. The faster the execution loop, the more empowered team members are to share results and iterate on code changes.

Faster test execution times allow faster troubleshooting, better test coverage, and quicker feedback.

Continuous integration

Continuous integration practices rely heavily on automated testing to maintain code quality. Integrating automated tests with CI/CD pipelines allows developers to receive feedback quicker once the code is committed. This results in quicker iterations and reduced time between development and deployment. The rapid feedback loop is essential in agile development environments, where continuous improvement and quick releases are critical.

Integrate automated functional tests into your CI/CD pipelines to enforce code checks at every release stage. This enables the validation of every code change and leads developers to catch issues earlier in the process, ensuring the application remains stable as new features and updates are introduced.

name: CI Pipeline

on:
  push:
    branches:
      - main
  pull_request:
    branches:
      - main

jobs:
  test:
    runs-on: ubuntu-latest

    steps:
    - name: Checkout code
      uses: actions/checkout@v2

    - name: Set up Python
      uses: actions/setup-python@v2
      with:
        python-version: '3.8'

    - name: Install dependencies
      run: |
        python -m pip install --upgrade pip
        pip install -r requirements.txt

    - name: Run tests
      run: |
        pytest --maxfail=1 --disable-warnings -q

Integrating automated tests into CI/CD Pipelines with GitHub Actions

Maintenance and scalability

Create a process for keeping test suites up-to-date and capable of handling growth. For example, a team can:

• Regularly update test cases to reflect changes to the application.
• Refactor scripts for efficiency and code uniformity.
• Scale test infrastructure as load increases.

Ongoing maintenance ensures that automated testing remains relevant and effective as applications evolve.

Use cases of automated functional testing

Some areas where automated functional testing particularly shines include:

Regression testing

Automated regression testing is precious in projects with frequent updates, as it quickly validates that existing functionalities remain intact. You can re-run tests after each update to ensure that code changes don’t introduce new issues. It is also critical in complex systems where changes in one area of the codebase can have unintended effects on other areas.

Smoke testing

You can use automated smoke tests to confirm basic stability before more extensive testing. These tests are designed to be lightweight and quick, providing immediate insight into whether the application's most crucial aspects are functioning as expected.

Load testing

Automated load tests can simulate multiple user interactions and assess the application's performance under stress. They evaluate the stability and ability to handle a specified number of simultaneous users or transactions without performance degradation. Automated load testing tools generate virtual users to simulate real-world scenarios, helping teams identify bottlenecks and understand how the application behaves under peak loads.

Complex scenario testing

Automated testing suits scenarios involving multiple steps and conditions that are too cumbersome and error-prone to check manually. You can write scripts for complex user interactions, dependencies, and workflows. Cover all possible paths and conditions, ensuring the application behaves as expected in every situation.

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Best practices in functional automation testing

Choose the right tools and frameworks.

Teams should evaluate options based on their specific needs, such as the complexity of the application, the types of tests required, and the level of support for different platforms.  As a first step, teams will assess current testing processes and identify areas where automation can have the most impact.

When selecting an automation approach, consider whether to use:

1. Record-and-playback tools, which capture and replay user interactions or
2. Scripted testing, where test scripts are manually written.

The two approaches aren’t mutually exclusive—they can be combined at the test or project levels. Videos can be recorded for each test, and depending on user configuration, they will only be saved if the test fails.

import { defineConfig } from '@playwright/test';
export default defineConfig({
  use: {
    video: 'on-first-retry',
  },
});

Balance automated and manual testing

While automated testing offers numerous benefits, automation and manual testing should be balanced for the best results. Certain types of testing, such as exploratory or usability testing, may still require human intervention to provide insights that automated tests cannot. As new features are being built, manual testing will likely be practical during prototyping and experimentation. Automated testing becomes viable as features become standardized and ready to be merged into production environments. This is because developers will have stable code against which to run tests and compare results.

When starting with automation, teams usually establish a baseline code release or code branch. This is usually the “main” branch, but different companies have different systems for releasing code. As a best practice, teams bake their testing suites into their code release process using CI/CD integrations.

Indeed, CI/CD integrations sit at the heart of most automation strategies. The manner in which a team integrates new code dictates how that code should be tested. Consider the code merging strategy used when adding CI/CD to a codebase. Does your team use a Gitflow strategy, trunk-based branching, or a custom process? While trunk-based workflows are generally considered most conducive to CI/CD integrations, any strategy can work given the proper considerations. Ensure that feature branches do not diverge too wildly from their base branch. Build code to be as modular and testable as possible.

Clear and detailed test case documentation

Develop detailed test cases covering all possible interactions and expected outcomes. Create modular and reusable test frameworks that you can adapt to different scenarios. This ensures that tests are easy to understand and maintain, reducing the risk of errors or missed requirements.

Adopt clear naming conventions

Use descriptive and consistent naming conventions for test cases, scripts, and variables. Clear naming conventions help teams quickly understand the purpose and scope of each test. For example:

• Test Cases: Name test cases based on their functionality and expected outcome, such as Login_ValidCredentials_ShouldSucceed or Checkout_InvalidCoupon_ShouldDisplayError.
• Scripts: Use descriptive names for scripts, like UserAuthenticationTests or PaymentProcessingValidation.
• Variables: Adopt meaningful names for variables, such as userEmailAddress instead of email and orderTotalAmount instead of total.

Prioritize essential tests

Focus on testing critical functionalities first to address the most significant risks. Prioritizing helps teams allocate resources effectively and ensures key areas are covered.

This is most relevant in large code bases, where test suites may take hours or even days to complete. There may be many cases in which an engineer wants to run a select subset of tests and get feedback about code within minutes. Having a plan in advance for this will prevent headaches during troubleshooting.

It may not be as simple as running a single command to isolate tests. Relevant test information needed to troubleshoot an issue may be scattered across services, components, or networks. External services and third-party providers may also be factors and inter-service requirements—do we need an API test environment running to troubleshoot issues in our new mobile client?

Documenting and planning around these needs is the first step of building a strategy for real-time decision making based on our architectures. As a best practice, document and iterate these processes. These processes can then be automated and scaled as QA requirements evolve over time.

Set appropriate test frequencies and triggers

Determine the frequency and triggers for automated tests based on the development cycle and project requirements. Setting appropriate test frequencies ensures that tests are run often enough to catch issues early without overloading the testing process.  

Cost is the first factor when determining test run frequency. While obtaining the most test coverage possible is ideal, resource consumption quickly becomes a factor. Imagine the (improbable) event that a team used an automated script that ran their test suite every second of every day in cloud environments. Their compute usage costs by month’s end would be eye-watering, and they would likely trigger resource consumption quotas from cloud service providers.

More realistic approaches for most teams include:

• Scheduled Builds - hourly, semi-hourly, or daily test suite runs on production code branches
• Build on Commit - every code commit triggers actions and tests. This can be customized so that only specific branches trigger CI/CD workflows.

Qualiti's approach to automated functional testing

Automated functional testing often involves challenges such as environment setup, CI/CD integration, and managing test complexity. If not adequately addressed, these obstacles can hinder the effectiveness and efficiency of testing processes.

Qualiti offers solutions for these critical challenges, providing targeted approaches to streamline and enhance testing workflows.

Test Environment Setup

Qualiti's Test Plans feature simplifies setting up and managing test environments, ensuring consistency and reliability across test runs. This solution reduces the complexity and time required for environment setup, allowing teams to focus on testing rather than configuration.  

Continuous Integration and Deployment

Qualiti offers seamless integration with CI/CD pipelines, enabling automated tests to run during development and deployment. This integration ensures that code changes are continuously validated, helping teams catch issues early and maintain high software quality.

AI-Powered Automation

Qualiti leverages AI to automatically generate and maintain test suites, reducing the manual effort required for test case design and maintenance. AI-driven automation helps teams create more efficient and effective test cases, improving coverage and accuracy while reducing the time spent on test creation.  

Accessibility Through Low-Code/No-Code Solutions

Qualiti’s low-code/no-code approach makes automated testing more accessible to team members across different technical backgrounds. This approach allows non-technical users to participate in the testing process, fostering collaboration and improving the overall quality of the application.

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Last thoughts

Automation is an ongoing process that requires some up-front planning and care. Automated functional testing allows us to catch bugs early, speeding up release cycles and smoothing development. Beyond that, it is designed to give teams (and stakeholders) confidence in their software’s reliability.

The journey to successful automation involves thoughtful planning and continuous refinement. When building your strategy:

• Establish clear testing goals and prioritize critical functionalities
• Choose the right tools and frameworks that align with your team's skills and project needs
• Create descriptive test case naming conventions for clarity and maintainability
• Implement a balanced approach between automated and manual testing
• Regularly update and maintain your test suite to keep pace with software evolution

The journey to successful automation involves choosing the right tools, writing thoughtful test cases, and keeping everything up-to-date as the software evolves. When teams get it right, automated testing becomes a powerful ally, helping. It’s about working smarter, not harder, and ultimately delivering high-quality software that teams can be proud of.