Artificial intelligence (AI) in test mechanization is the newest trend in quality assurance. Unfortunately, testing in general, and test automation in particular, seems to have caught the “everything is better with AI” bug. With AI, machine learning, and neural networks all the rage, it is perhaps inevitable that AI will somehow find its way into test automation. In this article we will let you know AI in test automation.
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Enter AI: Hype vs. Reality
AI technology in its current form is all about using machine learning procedures to train models on large amounts of data and then using the trained models to make predictions or achieve the desired outcome. Almost all AI fit this admittedly simplified description. For us, however, the main question arises here:
Will AI be able to generate and update test cases automatically? Find mistakes? Advance code coverage?
The answer to that question is distant from clear, as we are at the peak of the AI hype cycle. However, one specific sub-area, deep learning, has generated much of this excitement.
How Does Machine Learning Produce Automated Tests?
Training – In the training phase, the machine learning model must fit on a specific administrative dataset, including the codebase, submission interface, logs, test cases, and even specification documents. However, an insufficiently large training dataset can reduce the algorithm’s efficiency.
Some tools have pre-trained models that are efficient through continuous learning for specific applications, such as UI testing, allowing for generalized knowledge to be cast-off in a particular organization.
Output/Result Generation – Depending on the use case, the model makes test cases, checks existing test cases for coverage, completeness, and code correctness, and even runs tests. Either way, a tester should review the generated output for validation and ensure it is usable.
If we use the self-driving car analogy, the results look more like assisted driving than an actual driverless car.
Continuous Improvement – As an organization uses the tool regularly, the training data will continue to grow, potentially increasing the training networks’ accuracy and efficiency. In short, the AI system is constantly learning and improving.
Application Of AI In Test Automation
Let’s take a closer look at some uses of AI in test automation, counting unit testing, UI testing, API testing, and maintaining a test suite for automation.
Create and update unit tests
Unit testing often used as a share of Continuous Testing, Continuous Integration/Continuous Distribution (CI/CD) in DevOps, can be a pain in the…asteroid belt.
Developers typically spend a lot of time creating and maintaining unit tests, which isn’t nearly as fun as writing application code. In this case, AI-based products for automating unit test creation can be helpful, especially for organizations that want to introduce unit tests late in the product lifecycle.
AI-powered automated unit tests are a step ahead of generating model-based automated unit tests with static or dynamic analysis. The tests developed in this way are actual code, not just stubs.
AI-powered unit tests can generate very quickly, which is helpful for a large existing code base.
Developers only need to modify the tests and can quickly set up the unit regression suite.
AI-generated unit tests reflect the code they are ample on. You still can’t guess the code’s intended functionality. If the code does not behave as expected, the unit test made for that code will replicate this unexpected behavior.
That is a significant disadvantage since the purpose of unit testing is to enforce and verify an implicit or explicit contract.
Unit tests created using machine learning can break existing working unit tests, and it’s up to a developer to ensure this doesn’t happen.
Developers have to write their tests for complex business logic.
Automated User Interface Testing
It is one area where AI is starting to shine. For AI-based UI testing, test automation tools analyze the DOM and associated code to discover object properties. They also use the same recognition techniques to navigate the application and visually inspect objects and UI elements to create UI tests.
Additionally, AI test systems use investigative testing to find bugs or variations in the application’s user interface and generate screenshots for later review by a QA engineer.
Automated UI testing can result in more code coverage.
Minor UI differences do not cause the test suite to fail. Product AI models can handle it.
The number of stages, app versions, and browser versions matters for any modern app. It’s unclear how well the AI-based UI automation performs under these conditions. However, cloud testing tools can run tests in parallel, so this will be an exciting area to watch!
Using AI To Assist In API Testing
Even without AI, API test automation is not trivial, as it requires an understanding of the API; you can configure the tests for various scenarios to ensure the depth and potential of coverage.
Current API test automation tools such as Tricentis and SoapUI log API traffic and activities to analyze and create tests. However, changing and updating tests requires testers to delve into the details of REST calls and parameters and then inform the API test suite.
The devices also use existing tests to understand API relationships, use them to understand API changes, and update existing tests or create new tests based on scenarios.
For inexperienced testers or those with no programming experience, this could be very helpful to give them a head start.
Again, change management would be much easier since at least some API changes can be artificial by an AI automation tool.
API testing is generally challenging to set up, and few tools offer machine learning-based capabilities in this area—those who seem to have pretty rudimentary skills.
Automation Test Maintenance
AI-based tools can assess code changes and fix multiple existing tests that don’t match those changes, especially if those code changes aren’t too complex. Updates to UI elements, field names, etc., no longer have to break the test suite.
Some AI tools monitor running tests and test modified variants for failed tests, choosing UI elements based on the best fit. You can also check the test coverage and fill in the blanks if necessary.
AI-Based Test Statistics Generation
Test data generation is an additional promising area for AI models. For example, machine learning can quickly generate datasets such as B. personal profile photos and information such as age and weight based on training machine learning models that use existing production data sets for learning.
In this way, the generated test data is very similar to the production data, which is ideal for software testing. The machine learning model that creates information is called a Generative Adverse Network (GAN).
Products Providing Machine Learning Based Test Automation
Check out the tools below if you’re looking for software that uses machine learning to run and track automated tests. Many include open-source or no-code options to meet the needs of your testing team.
- Applitool eyes
- A function
- My beauty
- Parasoft SOA testing
Artificial intelligence has significantly impacted testing tools and means in general and test automation in particular. An overview of current tools showing promise for AI shows that many new features are extra, but some are still maturing.
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