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5 factors behind successful test automation strategy
Chief Executive Officer
A well-tuned test automation strategy reduces development-related costs by 30% on average. This is not to mention the significant increase in efficiency and software quality.
Have you ever used a GPS navigator while driving? Chances are you did. GPS splits you and traffic jams and finds the most efficient route. Same way, test automation helps you identify and fix software defects early, saving the budget allocated to bug fixes.
Test automation is the process of using software tools to execute pre-scripted tests on a software application. And we are surely making this approach indispensable. With the demand for rapid releases and continuous integration, manual testing methods struggle to catch up. Continuous improvement in test automation makes the execution of complex test suites effortless and efficient.
Microsoft implemented a test automation framework years ago. This move halved testing time and improved test coverage by a third.
Want the same? Explore the 5 key factors behind a successful test automation strategy in this article.
Factor 1: Clear objectives and scope
You don't start a long-distance trip without an understanding of a clear finish point. Similarly, your test automation efforts need a guiding factor. Without well-defined goals, it becomes challenging to measure success.
Know the goal
Without a clear “testing destination”, your budget may go down the drain. Let's list some possible objectives and align them with overall business goals:
Reduce time-to-market: Automate repetitive and time-consuming tests and, this way, get faster deployment of new features. If you operate in a highly competitive market (like AI/ML apps, fitness/sport, manufacturing) speed can become a significant differentiator for you.
Increase test coverage: Automate a larger number of test cases, including those that are difficult to perform manually.
Improve product quality: Find defects earlier — get a better quality of the final product.
Optimize resource allocation: Automate routine tasks and beef up team efficiency. Make testers focus on exploratory and complex testing scenarios instead of mulling over repetitive tests.
Ensure consistent testing processes: Automate and standardize testing procedures to eliminate human error and maintain consistency across different testing cycles and teams.
Know the scope
Don’t automate tests that are unstable or frequently changing. Think of the future team: your testing team should regularly maintain and update test cases, and features that are still in flux can be a significant pain in the neck.
Prioritize stable areas of the application where automation can provide long-term benefits without excessive upkeep. Not all tests will bring about tangible results, and selecting the right ones can maximize ROI.
Regression tests: Automate regression tests to ensure that changes to the application don't break existing functionality.
Smoke tests: Automate smoke tests to verify that the application is stable and functional after deployment.
End-to-end tests: Automate end-to-end tests to simulate user journeys and ensure that the application works as expected.
> For startups: Start off with smoke tests and critical path tests. The former verifies the app's basic functionality and makes major features work as expected after each build. Automating these tests helps detect fundamental issues quickly without significant upfront investment.
> For established companies: Focus on automating regression tests and integration tests. The former ensures that new code changes don't negatively affect existing functionality. It is essential when dealing with a large codebase and frequent updates. The latter verifies that different modules or services within the application work together correctly.
Factor 2: Selection of the right tools and frameworks
The right tools enhance efficiency, while the wrong ones make your time and resources go south. Let's explore common and proven test automation tools and frameworks. But first, let's run through the criteria for selecting the tool.
Key criteria for automation tool
Compatibility with technology stack: Ensure the tool supports the programming languages, platforms, and frameworks your application uses. For example, if your application is built with Angular, you may want to choose Protractor or Cypress. For cross-platform mobile apps, Appium is a more popular choice.
Ease of use: Your team should easily adopt the new tool. A sharp learning curve can slow down the adoption process. Many use open-source options like Selenium WebDriver since these tools have extensive community support. Want to get up to speed? There are old-hand PROs to help.
Scalability: Can your tool handle the application's growth and test suites? You may stop by BrowserStack which allows for running tests across multiple browsers and devices simultaneously.
Integration capabilities: The tool should integrate seamlessly with your existing development ecosystem, including CI/CD pipelines, version control systems, and project management tools. Choose tools that work well with platforms like Jenkins, Git, and Jira.
Cost adequacy: Calculate the cost of ownership (licensing fees + maintenance + training costs, etc.). Open-source tools may reduce upfront costs but consider the investment required for training and potential limitations.
Which frameworks may fit your goals?
Why robust framework matters
Reusability of code: Promotes writing reusable functions and modules, reduces duplication and effort.
Maintainability: Simplifies updates to test scripts when the application changes.
Scalability: Supports the addition of new test cases and accommodates growing test suites without significant rework.
Reporting and logging: Provides detailed test reports and logs for easier debugging and analysis.
Examples of popular frameworks
Data-driven
Separates test scripts from test data. You can run the same test script with multiple data sets. Test data is typically stored in external files (Excel spreadsheets, CSV files, databases, etc.).
Why try: You increase test coverage with various input values and conditions without modifying the test scripts. Maintenance is simpler since changes in test data don't require changes to the code.
→ Useful for form validations or transaction processing; eventually, for all apps where you test different inputs on the same functions.
Keyword-driven
Here, we have particular actions to perform on the app. And we use keywords to represent those actions. We write test cases using these high-level keywords, which are linked to the underlying code.
Why try: If your testers have limited programming knowledge, they still can write adequate test cases. The framework improves readability and makes test scripts more intuitive, facilitates collaboration between technical and non-technical team members.
→ Ideal when you have a diverse team where not everyone is proficient in programming.
Behavior-driven development (BDD)
Focuses on the app’s behavior from the end-users’ perspective. We write test cases in plain language using a specific syntax (e.g., Gherkin) and make them understandable to all stakeholders.
Why try: Because after all, all we do is for users. This framework allows users to observe your product by users’ eyes. Also, to align all people's understanding of the requirements.
→ Cucumber and SpecFlow help bridge the communication gap between technical and non-technical team members and integrate with Java and C#.
Insights to take into account
> Match tools and frameworks to project needs: Assess your project's specific requirements, team expertise, and long-term goals. Find your specific flaws in the process, find your strong sides as well.
> Consider community support: We have all been there when we needed some support. Active communities offer it, in addition to regular updates. Best practices are best practices.
> Try first, pay after: Paraphrasing the famous ad quote, test the tools and frameworks before going all in with them. A small, controlled environment keeps you calm and with clear thinking to evaluate tools’/frameworks’ effectiveness.
> Training and skill development: Train your team to use the selected tools and frameworks. Want to maximize the return on investment and ensure smoother implementation? Invest time, effort, and money.
Factor 3: Integration with CI/CD pipelines
CI/CD integration with test automation validates the product at every stage and catches bugs before they progress further. This reduces manual intervention and speeds up the release process.
Advantages:
Faster releases: Automated tests run automatically with each code commit — teams can release updates more frequently without harming quality.
Improved reliability: Continuous testing catches defects early and reduces the risk of failures in production environments.
Enhanced collaboration: Developers receive immediate feedback on their code changes and this way, enhances accountability and ownership.
The 2021 State of DevOps Report: high-performing companies integrate automated testing into their CI/CD pipelines. Proper integration and high-level DevOps maturity influence a team's efficiency and product quality.
How-to mini guide: Automated tests integration
1/ Set up a CI/CD tool
Consider all the factors for effective test automation mentioned in this article and choose a CI/CD platform that strikes the balance between your technology stack and team expertise. Chances are you’ll stop by Jenkins, GitLab CI/CD, CircleCI, or GitHub Actions. They automate the entire cycle: the building, testing, and deployment.
2/ Tune automated test execution
Add automated testing stages to the CI/CD pipeline. After the build process, add stages for running unit tests, integration tests, and end-to-end tests. This way, you can rest assured the code changes are verified at each level.
3/ Define test environments
Docker is here to help. It creates consistent and isolated test environments identical to production. This is the easiest way to reduce environment-related issues.
4/ Implement test reporting
Allure Report, another tool, or built-in reporting features of your CI/CD platform. Anyway, detailed reports will help teams quickly identify failed tests and understand the causes. Then — proper and prompt fixes.
5/ Set up notifications and alerts
You must stay alert to system failures. So, organize the way to receive notifications via email or messaging apps (Slack, MS Teams, etc.) when tests fail.
6/ Optimize for parallel execution
Leverage the CI/CD tool's ability to run tests in parallel. This optimization reduces overall testing time and speeds up the feedback loop.
Hands-on tips for startups
→ Start small and iterate: Start off automating the most critical tests (unit tests for core functionalities). Gradually expand test coverage as the team gains confidence and experience with the novelty.
→ Use cloud-based CI/CD services: For startups with limited infrastructure, cloud-based platforms like CircleCI, Travis CI, or GitHub Actions deliver scalable solutions without the need to maintain servers.
→ Invest in training: Strong team proficiency = fewer failures and better results. Your team members must be familiar with CI/CD concepts and tools. To accelerate adoption and reduce errors, create possibilities for your team to learn and develop.
→ Automate environment setup: Use Infrastructure as Code (IaC) tools like Terraform or Ansible to automate the provisioning of test environments. This practice ensures consistency and saves time.
→ Monitor and refine: Regularly review pipeline performance metrics such as build times and test durations. Identify bottlenecks and optimize configurations to improve efficiency.
Factor 4: Test data management
Quality test data is a reflection of real-world scenarios. It helps create special condition and environment for tests to run. This way, we will get valid results and genuine defects to fix.
> Example: Healthcare and financial applications. Here, realistic patient data with various medical histories is crucial as the test data that doesn’t reflect actual patient profiles triggers inadequate critical bugs. And privacy defects (not only them!) might go unnoticed. Similarly, in a financial application, testing with authentic transaction data helps uncover issues in processing deposits, withdrawals, or fraud detection mechanisms.
As a summary, realistic test data is vital, because it impacts:
Accuracy of test outcomes: Automated tests rely on accurate data to produce valid results.
Realistic testing scenarios: Representative test data simulates real user behavior and – which is more important – edge cases.
Regulatory compliance: In industries like healthcare and finance (an example is above), compliance with HIPAA or GDPR is a major requirement for further development.
Performance testing: Inaccurate simulation of load conditions in a web application, for instance, may not reveal performance bottlenecks that could occur under actual user loads.
Unrealistic test data can result in false positives or negatives. It may also cause automated tests to pass when they should fail, and guess what will happen in the production.
Strategies for test data management
Sources of test data
1. Production data copies
Quality: High realism (actual user behavior and data patterns).
Access level: Restricted due to privacy laws and compliance regulations (e.g., GDPR or HIPAA).
Requires thorough data masking or anonymization to protect sensitive information before use in testing environments.
2. Synthetic data generation
Quality: Customizable to meet specific testing needs.
Access level: Unrestricted since data is artificially created.
Mockaroo or Data Generator can produce large volumes of data tailored to the scenarios you need. Yet, it may lack the unpredictability of real user data.
3. Third-party datasets
Quality: Varies depending on the provider and relevance to your industry.
Access level: Subject to licensing agreements and potential costs.
Kaggle Datasets (for ML), Data.gov, or Quandl offer diverse data collections. They are definitely worth your attention, but they may misalign with your application's specific requirements.
4. Open-source datasets
Quality: Generally good for generic testing purposes.
Access level: Freely available to the public.
UCI Machine Learning Repository and Kaggle provide datasets that will help you in the initial testing. Yet, they might not cover all edge cases relevant to your needs.
Techniques for managing and creating test data
1. Data generation
Test data management in automation implies using data generation tools. Synthetic data isn’t a sin, especially if it meets specific criteria. But this way, you can scale and customize. For example, generate thousands of user accounts with varying attributes to test the performance and reliability of a social media platform under load.
2. Data masking (anonymization)
When using production data, apply masking techniques to anonymize sensitive information. Informatica or IBM Optim can replace personal identifiers with fictitious but realistic values to maintain data integrity and comply with privacy regulations.
3. Data subsetting
Extract a representative subset of the production database to use in testing. This reduces the data volume while preserving critical relationships and characteristics. It's an efficient way to manage storage and improve test execution times.
4. Test data virtualization
Virtualization tools create virtual copies of test data to develop multiple testing environments where you can share data without duplication. Consistency at its best (and without overusing your storage).
Best practices for ensuring data consistency and relevance
Regularly refresh test data: Outdated information inevitably leads to irrelevant test results and overlooked defects due to schema changes or new business logic. As a result, bugs lower user satisfaction or even bring about critical security issues.
Maintain data integrity: Data relationships and constraints must be preserved in the test data. Inconsistent or corrupt data can cause tests to fail inaccurately. Therefore, you waste time looking for sinners in heaven (troubleshooting non-existent issues).
Version control test data: Store test data scripts and datasets in a version control system (e.g., Git). Teams can track changes and roll back to previous versions if necessary.
Automate test data management: Incorporate test data creation and provisioning into your automation scripts and CI/CD pipelines. Automation reduces manual effort, minimizes errors, and ensures that the correct data is available when tests are executed.
Compliance and security: When handling sensitive data, always adhere to legal and regulatory requirements. This means strict access controls, encryption, and auditing. This way, you can shun unauthorized access and ensure compliance with standards (GDPR, HIPAA, PCI DSS, etc.).
Factor 5: Maintenance and continuous improvement
Maintenance matters
The project will evolve, bugs will be fixed many times, you will add updates — automated tests must reflect these changes. Poor maintenance will bring about test failures, false positives, or missed defects.
For instance, LinkedIn experienced significant issues after a major platform overhaul. Their automated tests had not been updated to reflect new user interface changes. The company had to scramble to address these issues post-launch: oversight in test maintenance can lead to costly repercussions.
Another example is Netflix, which employs a robust strategy for maintaining its automated tests. When introducing new features or making changes to existing ones, their QA team conducts regular reviews of test scripts. What can we say if the company deploys thousands of times per week? A proactive approach always pays off.
Improve, improve, improve
Test automation processes are an ongoing effort that requires strategic approaches. Key strategies for continuous improvement:
1/ Feedback loops
Regular communication touchpoints between testers, developers, and stakeholders open doors for discussing test results, challenges, and improvements.
Pros:
Facilitates immediate attention to defects and testing obstacles.
Promotes a shared understanding of quality goals across teams.
Cons:
Sometimes it’s quite difficult to find a suitable time for all involved.
Focus is the main advantage and the main barrier at the same time. How many times did you lose your bearings during meetings? Well, you are not alone.
2/ Performance monitoring
Continuously track the ROI of test automation and other metrics (test execution times, failure rates, etc.) to spot trends.
Pros:
Identifies bottlenecks and optimizes test performance.
Teams make conscious improvements.
Cons:
May require sophisticated tools and expertise to analyze data.
Continuous monitoring = additional resources.
3/ Regular code reviews
Systematically review test automation code to ensure best practices and coding standards are upheld.
Pros:
Enhances maintainability and reduces technical debt.
Encourages learning and consistency among team members.
Cons:
Requires dedicated time from senior team members.
Might slow down the development of new test scripts if not managed efficiently.
4/ Adoption of new technologies
Catch up with the latest tools, frameworks, and methodologies. And don’t limit yourself to AI/ML.
Pros:
Advancements improve you and your testing capabilities.
Grow way faster, receive better support from technologies.
Cons:
Need for training to effectively use new tools.
New technologies may not seamlessly fit into existing workflows.
5/ Test script refactoring
Update and optimize test scripts to improve efficiency and reduce redundancy.
Pros:
Cleaner code is easier to update and troubleshoot.
Cons:
Requires time that might be limited during tight deadlines.
Changes must be carefully tested to avoid new issues.
To sum up
2024 State of Continuous Testing revealed that a well-planned test automation strategy helps teams achieve significantly higher test automation coverage and reduce testing time.
Successful test automation includes sizing up the five key factors:
Clear objectives and scope
Selection of the right tools and frameworks
Integration with CI/CD pipelines
Test data management
Maintenance and continuous improvement
By considering these factors you can ensure that your test automation effort aligns with the business objectives. If you seek expert guidance to strive for the best ROI of test automation, DeviQA is ready to assist.
Contact us to discover how we can help.