What is Automation Testing and Why is it Important?
Automation testing refers to using tools and frameworks to execute repeated tests that validate application functionality and performance. It aims to take over repetitive tasks that are difficult and tedious to perform manually.
The global automation testing market is growing at a CAGR of over 18.5% and expected to reach $35 billion by 2026 according to Mordor Intelligence. The benefits of incorporating test automation include:
- Faster feedback cycles – early detection of defects
- Enhanced test coverage through large scale regression suites
- Consistent and reliable test execution without human errors
- Improved efficiency allowing focus on complex test scenarios
- Maximized test effectiveness and ROI
Types of Testing to Automate
Different testing types that are well suited for automation:
Unit Testing
Automated unit tests validate functionality of isolated code components like functions and classes. Executed as part of CI/CD pipelines, they check basic program logic and output. Popular Java unit testing frameworks include JUnit, TestNG and Jest. For .NET – MSTest and NUnit.
Integration Testing
Automated integration tests verify combined modules of an application work correctly. They validate communication between integrated components. Useful for microservices and API driven applications. Common tools are Postman and Cucumber.
Functional Testing
Automating functional validation ensures features offered by the system work as expected from an end user perspective. Helps establish confidence in build quality during frequent code changes. Selenium and UFT are popular commercial tools.
Regression Testing
Automated regression suites are the most critical as they repeatedly validate software behavior and output has not changed with new enhancements. Preventing regression defects is vital for production quality. Selenium, Cypress and TestComplete excel in this space.
Performance Testing
Automated load and stress testing helps benchmark and monitor application performance metrics like response times, throughput and resource utilization under varying user loads. Common open source tools include JMeter and Gatling.
Visual Testing
Automated visual regression testing catches UI, UX and layout changes in web or mobile apps that break front end appearance. Powerful for testing across multiple viewports, devices and OS versions. Applitools, Percy and BrowserStack offer robust visual testing capabilities.
Getting Started with Test Automation
Define Strategy and Scope
Outline what you want to achieve via automation and build executive buy-in. Prioritize what core regression suites or flows to automate first based on risk, value and complexity. Expand scope in iterations.
Assess Tool Requirements
Analyze key parameters when evaluating test automation software:
- Application types – web, mobile, APIs
- Environment support – OS, browsers, devices
- Ease of use – specialized skills, coding, record and playback
- Reporting and analytics
- Integrations with CI/CD and ALM tools
- Vendor support model – community/commercial
Shortlist tools aligning closest to your needs.
Build Initial Test Suite
Start small by automating workflow test cases offering maximum business value yet spanning critical parts of the application. Focus on areas with frequent regression issues first. Expand test coverage gradually after establishing initial automation.
Execute Tests Continuously
Trigger automated test suites on every code commit, merge and release to provide fast feedback on software quality. Integrate them into developer workflows and CI/CD pipelines for preventing defects and improving cycle times.
Enhance and Maintain Suite
Expect test maintenance overhead to keep pace with evolving application functionality. Analyze test results trends to improve reliability, performance and coverage. Continuously refactor and enhance tests scripts.
Automation Success Best Practices
Test Automation Frameworks
Well structured automation frameworks promote test creation, execution, reporting and maintenance best practices e.g. shared test data sources, object repositories, custom libraries and more. Some popular test frameworks:
- Selenium – For web application testing
- Appium – For native, hybrid and mobile web apps
- Espresso – For Android app test automation
- XCUITest – For iOS test automation
- Postman – For API testing
Reliable Test Data Strategies
Carefully designed test data and environment setup is key to stable test automation. Static test data maintains referential integrity and consistency across test runs. Test data generators offer more flexibility. Isolate test data from production to prevent corruption.
Debugging Automation Failures
Effective troubleshooting processes help diagnose root cause of test automation failures faster:
- Logs analysis
- Screenshots
- Videos
- Device streaming
- Historical failure trends
Fix test flakiness promptly through scripts enhancements and application improvements.
Reporting and Analytics
Visibility into automation effectiveness is vital for ongoing management. Customizable reports should provide execution status, test coverage data, past trends and other matrices required to showcase ROI.
Skills Development
Dedicated practice and learning around test automation concepts mandatory for teams. Training programs on tools usage along with developing programming abilities in scripting languages like Java, JavaScript or C# key for large scale automation success.
Automation Testing Challenges
While offering enormous benefits, test automation has inherent complexity teams must plan for:
No Automation Eliminates Manual Testing
Even with automation, human insight remains indispensable for specific types of testing:
- Exploratory testing
- Usability testing
- Ad hoc tests
- Final UAT signoffs
Organizations still need talent with strong technical domain skills and out of the box thinking abilities.
High Initial Investment
Budgets required for reliable test environments, tools, frameworks and team skills development implies higher startup costs compared to purely manual testing. The ROI accrues over longer time though as automation benefits compound after initial progression up the maturity curve.
Scripting Complexity
Testers must tackle challenges like popups, iframes, dynamic elements and asynchronous operations during script creation. Evolving UI‘s and responsive web design also lead to more frequent maintenance needs due to locater changes, timing issues etc.
Lack of Application Testability
Certain apps don‘t provide sufficient hooks natively to allow comprehensive test automation. Teams counter via improved instrumentation, automated workflow stitching tools, shadow DOM access etc. Promoting better testability across the SDLC minimizes overall difficulty.
Integration with DevOps
Merging automation capabilities within CI/CD pipelines helps shift testing left. But increased parallelization places higher infrastructure demands. Living symbiotically with Agile processes also requires alignment on iteration planning, automation scope change etc.
The Road Ahead
Test automation will continue seeing increased mainstream adoption driven by digital transformation, rising software complexity and customer experience focus. Commercial and open source tools will evolve to address scale, mobile, ease of use and analytics challenges better.
Testing teams must embrace automation judiciously with pragmatic expectations by augmenting existing skills. Maintaining the human touch alongside harnessing machine power iteratively is key to extract the full benefits of intelligent test automation!
