Anti-Patterns in Software Testing – Ways To Avoid Them

Artem Golubev

Software testing is an essential part of the Software Development Life Cycle (SDLC). It makes sure that applications meet quality standards, function correctly, and are free from critical defects. However, testing is often hindered by poor practices known as anti-patterns.

Anti-patterns are common but ineffective solutions to recurring problems that lead to inefficiency, increased costs, and poor software quality. So, before we discuss anti-patterns more, let’s understand what software testing patterns are.

Software Testing Patterns

Software testing patterns are proven strategies and best practices to design, execute, and maintain efficient and effective tests. These patterns help teams improve test reliability, scalability, and maintainability while minimizing redundant efforts. Some common testing patterns include:

• Test Pyramid: This pyramid emphasizes a balanced distribution of unit, integration, and UI tests for faster and more reliable feedback. Read: Test Automation Pyramid Done Right.
• Test Double: Includes Mocks, Stubs, Spies, Fakes, and Dummies to isolate dependencies and simulate behavior. Read: Mocks, Spies, and Stubs: How to Use?
• Data-Driven Testing (DDT): Uses external data sources (e.g., CSV, databases, JSON) to run test cases with multiple inputs. Read more about Data-Driven Testing.
• Behavior-Driven Development (BDD): Promotes collaboration by using human-readable test scenarios written in Gherkin (Given-When-Then). Read: What is BDD 2.0 (SDD)?

What are the Anti-patterns in Software Development?

An anti-pattern solution in software development may initially seem appealing, but it eventually creates more issues than fixes. Anti-patterns might be as simple as a lack of documentation for your feature or an automated test or as complex as a company culture that doesn’t encourage new ideas or approaches.

What Are Anti-Patterns in Software Testing?

Anti-patterns are poor practices or recurring problems that arise when designing, executing, or maintaining test cases. These issues hinder productivity, increase test maintenance costs, and compromise software quality.

Importance of Testing Anti-Patterns

Testing anti-patterns is important due to many factors, including:

• Lack of proper test strategy
• Time constraints leading to rushed test case design
• Over-reliance on UI testing
• Insufficient collaboration between developers and testers
• Ignoring test maintenance and optimization

By identifying and eliminating anti-patterns, teams can establish robust, scalable, and maintainable testing strategies.

End Goal? Robust Tests, Less Maintenance

End-to-end UI tests are notorious for being prone to errors, taking an excessive amount of time to complete, and sometimes being extremely difficult to diagnose when they fail. Fixing the anti-patterns that crop up in these kinds of tests may help make your tests more stable, more helpful in tracing down the sources of issues, and less stressful (and expensive) to maintain.

Common Software Testing Anti-patterns

Below are some anti-patterns to avoid while creating and maintaining test cases.

Poorly Defined Test Cases

A poorly defined test case is one that lacks clarity, does not specify expected outcomes, or contains vague instructions. When test cases are ambiguous, testers may interpret steps differently, leading to inconsistent test results and difficulty in defect reproduction. Additionally, if a test case does not include clear assertions, it may pass even when a defect exists, reducing the reliability of test execution. Testers might also spend excessive time deciphering unclear instructions, leading to delays and inefficiencies in the testing cycle.

• Impact: Leads to inconsistent test execution, misinterpretations, and difficulty in defect reproduction.
• How to Avoid: Write clear, structured, and well-documented test cases with explicit inputs, steps, and expected outcomes.

Hard Coding Test Data

Hard coding test data into scripts, automated tests, and production code is a common testing anti-pattern. Before a system has been launched, developers often generate static test cases by hard-coding variables and anticipating outcomes in the code. In practice, though, it is more common for these fixed values to evolve over time. Because the test cases haven’t been updated to reflect the changed numbers, even major bugs may go undetected for a long time. Whenever these values are updated, which may be fairly frequently if not properly managed, developers have to go in and make the necessary changes by hand, which is inefficient.

• Impact: Fixed test values cause test failures when system data changes, leading to increased maintenance.
• How to Avoid: Implement data-driven testing using external data sources like CSV, databases, or APIs.

Not Having an Automated Test Strategy and Tools

Manual regression testing is another common example of an anti-pattern. That doesn’t mean every single test has to be automated (in fact, that can sometimes become another anti-pattern), but at least all repetitive tests should be. Going through the same regression tests manually before every iteration isn’t just plain inefficient but also bears a higher risk of missing bugs due to human errors. The whole process becomes even more troublesome when a critical issue is discovered towards the end of a manual regression testing cycle: the bug has to be fixed, and testing has to start again from scratch.

The creation and upkeep of a set of automated test cases for regression is an excellent strategy for avoiding such anti-pattern. This allows teams to run thousands of test cases and get results overnight (or even within minutes depending on the tool and project) with great precision, and it speeds up the time-consuming manual testing process. It also eliminates the possibility of human error and makes tests much more dependable, making them much more efficient.

Creating a test automation plan that details which tests should be automated is one of the greatest strategies to steer clear of this anti-pattern. This implies that testers may choose the best automation tools depending on what is available and what best suits their requirements, leading to far more dependable tests. It also simplifies testing process upkeep, guaranteeing that teams may fully automate everything in record time with pinpoint precision. Read: Test Automation Playbook.

• Impact: Manual regression testing becomes inefficient, leading to delayed feedback and a higher risk of missed defects.
• How to Avoid: Define a clear automation strategy, choose the right tools, and focus on automating repetitive tests.

Hardcoding Environment Settings

Hardcoding environment settings are another common testing anti-pattern that always leads to more issues. Even though the configuration variables for hardware and software used to conduct tests are subject to frequent change, developers commonly build test cases assuming that they would be executed on a particular system. However, teams may employ devices with varying hardware and software setups, which might create complications during testing. Because of this, tests may fail for no obvious reason or yield incorrect findings, leading to the wasted effort spent attempting to track out the cause of the issue.

Selecting a setup that can be readily duplicated in the future is the best course of action when creating a functional test environment. Let’s say that the testing team is unable to get the same outcome. As a result, it will be difficult for them to reproduce errors in comparable circumstances, even if they are utilizing the same techniques and technologies.

Avoiding this and similar anti-patterns may be facilitated by allowing for global configuration of the testing environment with a single command. This eliminates the need for manual package installation, making testing considerably more dependable and reproducible. Since the test cases will be machine-agnostic, they will be significantly more consistent regardless of the hardware being used.

• Impact: Tests fail unpredictably across different environments, making debugging difficult.
• How to Avoid: Use configurable environment variables and containerized environments (Docker, Kubernetes) for consistency.

Tests Dependent on Conditional Assertions

Unreliable tests include those that rely on conditional assertions. This indicates that if an assertion is true, the test will pass, and if it is false, the test will fail. False positives become a problem when there are too many tests being run using conditional assertions, which may cause setbacks and a general drop in test success over time. This usually necessitates redoing the task, which takes considerably more time and energy. Read: Assertion Testing: Key Concepts and Techniques.

• Impact: Creates flaky tests with unpredictable outcomes, leading to false positives or negatives.
• How to Avoid: Use explicit assertions with clearly defined expected outcomes. Avoid depending on dynamic conditions in assertions—ensure tests have consistent pass/fail criteria.

Over-Reliance on UI-Based Tests

Many teams mistakenly focus too much on UI automation while neglecting unit and API tests. UI tests are slow, fragile, and highly dependent on front-end changes, making them difficult to maintain. Since UI elements frequently change, automated tests that interact with these elements often fail, requiring constant updates. Additionally, UI tests take significantly longer to execute than unit or API tests, slowing down test feedback loops. If a test suite is heavily reliant on UI testing, debugging failures become more complicated, as the root cause could be anywhere in the system. Excessive UI testing also leads to bottlenecks in CI/CD pipelines, delaying deployments. While UI testing is essential for end-to-end validation, it should not be the primary focus of a test strategy.

• Impact: Increases test flakiness and maintenance costs due to UI changes, makes debugging difficult, and failures may stem from frontend instability rather than actual defects.
• How to Avoid: Follow the Test Automation Pyramid—focus more on unit and API tests, with limited UI testing. Use UI-independent validation methods, such as API or database assertions, where possible.

Not Updating Test Cases with Application Changes

When test cases are not updated to reflect application changes, they become obsolete and unreliable. In fast-paced development environments, applications undergo frequent modifications—new features are added, old ones are removed, and workflows change. If test cases do not keep pace with these changes, they may fail incorrectly or miss detecting defects in updated areas. Unmaintained test cases waste execution time by validating functionalities that no longer exist. Additionally, outdated assertions and validation steps may cause valid functionality to be flagged as defective. This leads to misallocated debugging efforts, slowing down development and testing cycles. Unmaintained tests also contribute to test bloat, making it harder to prioritize critical test cases and optimize test execution time. Read: Decrease Test Maintenance Time by 99.5% with testRigor.

• Impact: Slows down test execution due to test suite bloat. Causes delayed defect detection, leading to higher bug-fixing costs.
• How to Avoid: Regularly audit and update test cases to align with application changes. Implement a test case review cycle where outdated tests are identified and modified.

Conclusion

Avoiding the pitfalls mentioned above is crucial when designing your QA testing strategy. These patterns might emerge throughout the testing process. By being aware of these hazards and how they affect testing, we may prevent the unnecessary expenditure of time and resources that inevitably results from failing to avoid them. On the opposite, following industry best practices will likely save you resources in the long run.

“We spent so much time on maintenance when using Selenium, and we spend nearly zero time with maintenance using testRigor.”

Keith Powe VP Of Engineering - IDT