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Software Testing Anti-Patterns and Ways To Avoid Them

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 suite, or as complex as a company culture that doesn’t encourage new ideas or approaches. 

In this article, we’ll go through some of the most significant issues in QA test automation and how to prevent them from happening – saving you from stress and countless wasted hours.

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.

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

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 anticipated outcomes into 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.

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.

Hardcoding environment settings

Hardcoding environment settings is 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.

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.

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.

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