Top challenges in test automation maintenance

The number one maintenance challenge is dealing with brittle, flaky tests. Brittle tests are those that break easily when the application changes. Flaky tests are those that pass or fail intermittently, making test results unreliable.

Brittle and flaky tests frustrate teams and erode confidence in test automation. When tests fail unexpectedly, engineers waste time debugging the tests themselves rather than investigating potential bugs. No one wants to deal with tests that cry wolf by failing when nothing is wrong.

The root causes of brittle and flaky tests include:

• Overly specific locators - Using locators tied to specific IDs, indexes, or text makes tests more likely to break when the UI changes.
• Test logic flaws - Bugs in the test code itself can cause intermittent failures.
• Asynchronous actions - Automated tests running too fast and not properly waiting for async operations like network requests to complete.
• Resource leaks - Test code not properly cleaning up after itself can cause instability over time.
• Test environment issues - Problems with test data, databases, or third-party services can cause tests to fail inexplicably.

Here are some strategies to make tests more robust and stable:

• Implement resilience patterns - Build wait states, retries, and graceful error handling into tests to cope with async actions and temporary blips.
• Isolate and modularize tests - Break tests down into smaller parts that can fail independently without cascading failures.
• Leverage page object model - Encapsulate page interactions into reusable page objects that localize changes.
• Generate dynamic, context-based locators - Generate locators at runtime based on content rather than static attributes prone to change.
• Setup test data carefully - Script setting up test data to avoid coupling between tests. Teardown test data after each test run.
• Monitor test runs - Track test metrics over time to find outliers exhibiting instability.

As the application changes over time, the test suite needs to evolve as well. New features need new tests, while outdated tests for removed functionality need pruning. Letting test suites stagnate makes them less effective at catching regressions.

The main maintenance activities around the test suite include:
Adding new tests - With each new feature or user story, new tests need to be added to exercise those behaviors. Expanding test coverage is key to detecting regressions.
Updating existing tests - When the application UI or workflows change, existing tests need to be updated accordingly. Outdated tests tend to become flaky.
Removing obsolete tests - Old tests for removed functionality should be deleted, as they have no value and just clutter the test suite.
Refactoring tests - Structure tests well using techniques like data-driven testing and the page object model to keep suites maintainable.
Optimizing test suites - Balance test suite size and running time with test parallelization, prioritization, and pipeline optimization.

To keep test suites nimble and aligned with application changes, leverage these strategies:

• Embed test suite maintenance into the development workflow - Make it a regular part of sprint routines rather than a separate activity.
• Review test coverage regularly - Identify gaps where new tests are needed as part of code reviews or with coverage reporting.
• Tie tests to requirements - Trace tests back to functional specs, user stories, or acceptance criteria to gauge relevance.
• Design for maintainability - Architect test code and frameworks in a modular way that isolates components.
• Utilize test generators - Generate test cases automatically from application metadata rather than hand-crafting everything.
• Analyze test metrics - Use historical test execution data to find areas needing improvement.

In addition to maintaining test code, teams need to maintain the frameworks, tools, environments, and other infrastructure underpinning test automation. Test infrastructure decays without care and investment.

Key aspects of the test infrastructure requiring maintenance include:
Test frameworks - Frameworks provide structure for test projects but need maintenance as new tools emerge and best practices evolve.
CI/CD pipelines - Automation pipelines that run tests need regular auditing to ensure optimal speed and reliability.
Test environments - Managing test environments, test data, and dependencies is essential for consistent test execution.
Test data - Realistic and adequate test data is needed to exercise different application scenarios.
Test reporting - Test reporting needs to provide useful insights into application quality and test health.
Supporting tools - Additional tools like test object repositories, mocks, stubs, and virtual services require maintenance.

Do the following regularly to keep test infrastructure optimized:

• Treat test code as production code - Apply robust engineering practices to test code including version control, peer reviews, and automation.
• Abstract test dependencies - Isolate tests from specific environments and data schemas to minimize maintenance.
• Standardize tooling - Reduce churn by standardizing on proven tools with strong adoption.
• Monitor performance - Track test environment health, automation speeds, and pipeline performance.
• Invest in tools - Allocate resources to purchase and build robust test infrastructure.
• Keep environments current - Upgrade test environments and tools to stay aligned with production.

When tests are not demonstrating value, it becomes hard to justify ongoing test automation investment. Tracking test effectiveness helps keep test automation focused where it matters most.

Aspects of test effectiveness to measure include:

• Code coverage - What percentage of the application code is exercised by tests?
• Test suite stability - How often do tests fail unexpectedly or flake out?
• Test failure analysis - Which tests fail most often? Are these high value tests?
• Defect detection - How many defects are caught by tests vs other means?
• Test maintenance costs - How much effort is required to maintain tests?
• Test execution duration - How long does the test suite take to run?
• Test optimization impact - How much does optimizing tests improve outcomes?

Here are tips for quantifying test effectiveness:

• Establish metrics aligned to quality goals - Focus on metrics like regression prevention vs broad coverage.
• Leverage automation - Automated reporting provides sustainable data for metrics.
• Analyze trends - Look at changes in metrics over time rather than one-off measures.
• Compare test outcomes across releases - Relate metrics to release quality like production defects.
• Tie metrics to business value - Calculate ROI of time savings vs manual testing.
• Review metrics in test retrospectives - Analyze metrics to determine improvements in test strategy.

Thorough documentation is essential for maintainable and scalable test automation. However, writing and updating documentation is often neglected.

Critical documentation for test automation includes:

• Test strategy docs - High-level overview of the testing approach and frameworks.
• Test plans - Details like scope, environments, risks, schedules, and metrics.
• Automation framework docs - Architecture, onboarding instructions, coding standards, and API reference.
• Test case specifications - Functional test scenarios, preconditions, test data, and expected results.
• Defect reports - Defect lifecycle management procedures and templates.
• CI/CD and environments - Test pipeline design, automation tooling, and environment details.

Maximize documentation quality and currency through:

• Embed documentation into team rituals - Schedule regular test documentation sprints and reviews.
• Treat docs as code - Store documentation alongside code in version control for easy updating.
• Automate report generation - Generate basic documentation like test plans automatically from metrics.
• Leverage wikis - Use project wikis for consolidating testing tribal knowledge.
• Enforce standards - Standardize templates and formats for improved organization.
• Make it visible - Use dashboards to display test plans, test status, and metrics dynamically.

Fixing maintenance issues as they arise is inadequate. The goal should be building and nurturing an effective test automation codebase sustainably.

Cultivating a maintenance mindset means:

• Designing for maintainability - Architect test code to minimize maintenance time over the long run.
• Setting aside regular time - Have disciplined routines for test suite upkeep like gardening.
• Reviewing metrics diligently - Fixing the process, not just surface issues.
• Engaging the whole team - Everyone contributes to test health through development practices.
• Advocating for investment - Allocating resources to build robust test infrastructure.
• Rewarding quality - Recognizing those who prevent issues proactively.

Maintaining automated tests is just as important as creating new tests. Leveraging robust test design, tracking test effectiveness, keeping infrastructure current, creating documentation, and adopting a maintenance mindset are key to managing this challenging but essential activity. The result is automated tests that provide value for years to come.

What strategies have you found most helpful for maintaining your test suites? Please share your insights in the comments below!