Optimizing Test Automation Frameworks Using AI

Test automation is actually quite an old concept—it's been around as long as software development itself. Over the years, various methods, tools, and frameworks emerged for automating application testing. 

However, the level of test automation we theoretically imagined hasn’t yet been fully achieved. Not much has changed in the past few decades in this area. Even today, despite the different frameworks available, test automation often requires as much effort as it did in the past. This is partly because apps are getting more complex, but the technology to test them hasn’t kept pace.

However, with the new addition of AI in this area, there’s potential for some major changes in test strategy—changes we haven't seen in the last few decades.

In this article, we’ll discuss what a test automation framework is and explore how AI can help transform automated testing strategy. We’ll also cover some essential points that testers should be aware of.

What is a Test Automation Framework?

Test frameworks outline a set of well-written guidelines and standard practices proposed by industry experts. These guidelines include proven strategies containing standard practices, methods, and tools. Typically, companies use it to make automation more stable. Furthermore, It directs QA professionals on how to perform tests effectively. 

These guidelines are not mandatory, but if a QA professional doesn’t carry out testing in accordance with these guidelines, they may overlook important areas. The framework provides a well-defined approach to writing test cases.   

If we simplify a test automation framework, it’s basically a collection of tools, practices, and steps that help QA professionals perform tests in a better way.

Even though it might sound like test automation means the tests are fully automated, that’s not entirely true. QA developers often need to try different options continuously and stay updated with new developments. 

The framework provides a guide for testers, outlining coding standards and how to handle test data, etc. For a tester, it is quite valuable in several ways. 

Let’s know how it benefits QA professionals and why it is important to build a smart test automation framework. Let’s know the benefits first. 

Benefits of a Test Automation Framework

1. Faster Feedback 

Writing scripts and managing them isn't always easy for QA developers. Manual testing is a process that cannot be completely eliminated; no matter how advanced automation becomes, there will always be some test cases where manual testing is necessary.

In such cases, many test cases can be offloaded to testing tools, and time-consuming work can be further simplified with tools having AI capabilities. For example, today’s AI tools can analyze specific patterns or codebases much faster, allowing QA professionals to focus more time on crucial tasks.

2. Improved ROI

 Test automation frameworks can cost anywhere from a few to several hundred dollars. However, using them can save your developers a significant amount of time. Automation tools can provide substantial value, particularly for repetitive, high-volume testing.

If we consider whether testers are more expensive or test automation tools, it might not be entirely appropriate to compare them directly. It's not entirely fair to say that you can replace a human QA professional with these tools. Also, if you're not using automation tools, it might end up costing you more.

On average, by using these tools, you can save a lot of time for your developers. As a result, they can significantly improve productivity. Many companies have found that their average ROI has greatly improved through these tools. 

3. Faster Delivery 

An effective framework is essential to speed up the quality analysis process for methodologies like Agile, which prioritize iterative processes. In today’s world, where a web presence is essential across desktops, wearable devices, and other platforms, businesses are focused on integrating all these systems to provide users with an immersive experience. 

With huge user bases, vast amounts of data, and the ever-expanding digital space, app development is becoming increasingly complex. Testing is as challenging as development itself in this scenario. Hence, having an excellent automated testing strategy has become crucial today.

Popular frameworks like Selenium Grid, TestNG, and JUnit can execute multiple tests simultaneously across different browsers at the same time.

Although human oversight is very important, many routine and repetitive processes can be significantly reduced through automation.

4. Improved Testing Accuracy 

Manual testing often misses a lot of errors because it depends on the capabilities of QA professionals. The more complex an app is, the more skilled professionals are needed. However, as the codebase keeps growing, professionals might overlook many things.

Testing frameworks act like allies, checking and catching those unnoticed issues in depth. Through these collective efforts, you can improve testing accuracy. Including these tools is one smart strategy for enhancing testing accuracy.

How to Built Automated Testing Strategy with AI

Artificial intelligence can make your automation framework more intelligent. For example, you can train machine learning models using specific organizational data, including the codebase and application interface, to generate test cases. You can also use other specific data to enhance this process. 

Many tools on the market now use machine learning algorithms to generate test cases, check existing test cases for code coverage, and even conduct performance tests to a significant extent. However, experts are still needed in all these cases.

Let’s talk about some applications of artificial intelligence in test automation.

Unit testing, which is a part of continuous testing and CI/CD pipelines, often requires a lot of time to author and maintain. Writing unit tests can be quite time-consuming compared to developing features. Using AI-based tools can make test creation much less painful.

AI can also be a better option for user interface testing. AI-based testing tools can interact with the application, analyze domain and code-related properties, and navigate elements efficiently using image recognition techniques.

For API testing, AI automation can record and analyze API activities and then create tests based on this data. AI-based API testing tools streamline this process significantly.

In test maintenance, you can leverage AI tools to help with code value assessment and fixing issues with existing tests. For instance, when code changes are not very complex, AI can simplify the process of updating and maintaining tests.

A Guide to Integrate AI into Your Testing Framework

If you want to include AI in your test automation framework, start by choosing an AI framework that fits your needs, like TensorFlow, PyTorch, or Keras, depending on what suits your team's expertise and requirements. After that, you can integrate testing tools like Selenium or any other testing tools of your choice. This is your first step in the integration process.

Relevant data is crucial for making AI effective. Without well-structured, relevant data, implementing an AI program might not be very beneficial. The more relevant data you have, the better your AI will perform.

You can use models for specific objectives, such as generating test cases, predicting defects, or optimizing test execution. Once you’ve defined your objectives clearly, you can select the appropriate algorithm. There are different types of algorithms, such as supervised learning and reinforcement learning. 

You should choose the algorithm based on your objectives. Your approach will be more effective once you select the right learning method. After setting up your model, you can train it and periodically evaluate its performance.

Importance of Human Supervision in Testing

Using tools without human supervision is like flying blind—it’s hard to know if you’re on the right path. If we leverage the capabilities of both, many aspects can be improved.

For example, testing tools can outperform manual testers in accuracy, but it’s not wise to publish code approved solely by tools. It is also important to decide on what process should be performed by the tools. 

Take the case of Knight Capital in 2012, which caused a huge stir. A small bug cost the company $450 million in just 30 minutes. This is an excellent example of poor software development and testing methods and highlights how crucial testing is—it's not something that should be solely offloaded to silly computer programs. They have still not reached a stage where an entire testing process can be offloaded to them. 

Build a Smart Automated Testing Strategy with AI

Automated testing often seems like it will completely take over the developers' responsibilities. It sounds like it automates testing, but that's not entirely true. Even with the most advanced tools today, these tools can only test certain parts of the software based on a predefined schedule, so human involvement is still necessary.

The value these tools provide varies for everyone, but one thing is certain: they make testing easier and save time. On average, a QA engineer can save about 15-20% of their time with these tools, and if used more effectively, even more time can be saved. Including these tools is definitely one of the smart strategies for business owners.

While the exact value they provide is hard to quantify, if you consider time-saving a valuable activity, then automated testing tools are essential.

A good test automation framework is more important than individual automated testing tools. This framework shows you how to use different tools and methods to improve and increase testing effectiveness. Ultimately, how well these tools work depends on the professional's skill.

Where Can AI be Used in Testing?

Testing tools integrating advanced AI help you write test cases quickly with increased accuracy. 

If we talk about AI, it can help in generating intelligent and self-healing test cases. This technology is quite amazing when it comes to evaluating mountains of code. Almost every framework is integrating artificial intelligence capabilities to make testing more effortless.

 With interactive prompts, developers can write custom tests with less effort. 

 Snapshot from testim website

Popular testing tools with AI can assess UI, codebase, and different needs. For example, if your checkout process changes, these tools can automatically adjust your test script. 

 AI is also making visual testing less complex as it can understand different screenshots and visual elements. AI can even analyze the new changes applied to UI to suggest areas of improvement. 

These features make them easy to use for less-technical testers to author professional like test cases.