A/B Testing: What is it and How can it Help your QA team?

Quality Assurance teams maximize product quality primarily by searching for software bugs, increasing accessibility, and implementing responsive designs.

However, improving a product’s quality may sometimes require adding all-new features to accommodate users’ needs. When this is the case, QA teams can work with developers to conduct A/B tests and determine which updates can best improve the product.

Instead of blindly implementing a new feature, A/B testing can help teams experiment with a variety of different optimization options and implement the best one based on user feedback and user-interaction data.

In this article, we will explain in detail how A/B testing works, how it improves products, and what to consider when developing an A/B testing strategy.

What is A/B testing?

A/B testing is a method of feature experimentation used to enhance the product based on user experience data. A/B testing begins with a hypothesis that a new feature will improve certain product metrics. To test this theory, we split the users into two or more groups.

The different user groups interact with different versions of the product. For example, the A group may use the original version without the newly hypothesized feature, and group B may experience the product with the feature under consideration.

Over time, we collect and analyze data from different metrics and determine the winning variation. 

How A/B Testing Improves Products

Relying on data and user feedback, A/B testing helps product managers gather evidence to determine the best way to enhance users’ experience with new features.

A/B testing also helps us see if users use new features as intended and if those features perform well. For example, we can determine if users register on our site more frequently if we present them with a signup form rather than guide them with an onboarding screen.

A/B Testing
A/B Testing

We can collect all this valuable data by tracking user events on our app or site and comparing user behaviors across the different variations. 

By learning from users’ data, we can increase user interaction and fidelity by improving the product based on the most successful optimizations. 

How we define an A/B testing strategy

Although users and the product itself can benefit substantially from A/B testing, the QA team faces several challenges when evaluating the product.

With different variations of the same app or different behaviors of one function, the number of testing scenarios grows dramatically, and the QA team needs to dedicate more resources to evaluating the different product variations.

However, with a strong A/B testing strategy, the QA team can mitigate these difficulties.

Our A/B Testing Strategy for QA considers the following:

  • Number of variations
  • Interaction between different variations 
  • Bucketing a user into a variation
  • Data collection

The number of variations: the number of variations relates directly to the number of test cases we will need. We recommend having at least one test case per variation. As a tester, you need to treat each variation as a different feature.

We also recommend testing the app without the new feature as a control. By removing all bugs in the original app, we can avoid potential inconsistencies in the experiment’s results.

Interaction between different variations: Depending on the number of features being tested, multiple experiments may need to run simultaneously. If variations from different experiments interact with each other, unpredicted bugs may arise.

The QA teams may have to evaluate many different possible interaction scenarios. By using testing strategies such as a table of decisions or combinatorial testing, the QA team can organize a method to address a large set of test cases.

However, if scenarios grow exponentially, the QA engineers will need to save resources and prioritize which scenarios to evaluate.

QA - Quality Assurance
QA – Quality Assurance

Bucketing a user into a variation: Because A/B testing randomly splits users into different variation groups, QA analysts may get bucketed into the wrong group while testing. If the QA team does not bring this issue up with the developers, they might have to retest multiple times before getting into the desired bucket.

The result may be a frustrated tester that wasted half an hour trying to get into the correct feature group. To prevent this, the QA team needs to work with the development team to create a tool for bucketing them into the desired state. 

Data Collection: An effective A/B feature test may require several weeks or months of data collection after deploying the variations to users. For the experiment’s success, we need to make sure we collect our data correctly.

Sometimes, we may use a third-party tool to collect interaction data, and there is little work needed on our end.

But, if you choose to develop a custom mechanism to track data, the QA team must verify that the tool correctly collects all events and associates them with their variation. The experiment will only succeed based on the analysis of the collected data.

Conclusion

A/B testing addresses and resolves users’ needs by experimenting with user behavior in real-time. After interpreting the results of A/B experiments, we can implement the features that prioritize the best user experience.

As QA testers, we ensure that the experiments work correctly by evaluating the different product variations and data collection methods for bugs. When working together, the QA team and developers can use A/B testing to design products optimized for their users.

Want more ways to improve QA? Check out our article on building an effective QA team.

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