How AI In Test Automation Works?

Kanika Vatsyayan
October 14, 2021
Banner AI in Test Automation

When we say “Artificial Intelligence,” it all ends up with a technology that has the potential to imitate human tasks with much more precision. More importantly, AI has entered almost every dimension of our day-to-day routine, right from the business operations, production process to your favorite binge-watching platform.

However, one of the most interesting trends surrounding the use of AI is the use of technology in testing and QA operations. To be precise, AI in test automation has turned to be a concept that has redefined the entire idea of Quality. Either general (manual) testing or test automation, AI has made things helped to fasten releases, minimized manual redundant testing, easy as smooth, right from writing scripts to catching bugs. Though AI, ML, and Neural network technologies have turned out to be an inevitable part of the automation software testing process, people are still curious to explore how AI aids test automation for software testing works.

How Machine Learning Produces Automated Tests?

Since we still have a long way to reach Artificial Super Intelligence, the present stage of AI is all about using Machine Learning Algorithms that can train on data to make predictions and support outputs. However, the most important question that revolves around the use of AI in Automation testing is how machine learning aids automated tests.

Let us try unfolding this query in parts:

1. Training

The primary step involves training machine learning models a.k.a algorithms on specific data. This data usually includes a codebase, interface details, requirements and specifications, logs, and test cases. In order to have precision with the outputs, it is vital to have the system trained on large datasets. Software testing companies even rely on tools having pre-trained models which allow continuous learning for applications having rich UI functionalities. Furthermore, the information obtained during training can be generalized to improve future projects.

2. Output Generation

Considering the use case, the machine learning models aid in generating test cases while checking the code coverage for existing test cases. The process even involves checking accuracy, completeness, and performance of the tests. However, the entire process needs manual checks and validation before a model is put to use.

For instance – AI helps in reducing the test case duplicity. Modern test management tools powered by AI and integrated with the test automation tools/frameworks can help you optimize the testing by checking and removing duplicate test cases.

Another such example of using AI is Error bucketing. With trending analytics & actionable intelligence within test reports, AI surfaces out the root cause of failures and facilitates logical grouping of errors. You can dig in steps that cause performance bottleneck and review trends with the automatic error-bucketing.

3. Continuous Improvement

The more frequent the use of the tool is, the more efficient the training data is. Thus, the need for continuous improvement requires the system to be fed with information from time to time so that the accuracy of the already developed trained networks can be improved.

The Various Applications of AI in Test Automation

Since we are done with how machine learning is used to produce automated tests, it’s time we should have a closer look at the applications of Artificial Intelligence in test automation services:

Create/Update Unit Tests

Often used in CI/CD operations, Unit tests could turn out to be a tricky process at times due to the extensive hours it needs for authoring and maintaining the test cases. Unlike writing application code, running unit tests often get consuming. AI can play a significant role in creating unit tests and improving the overall product lifecycle.

More importantly, AI-based automated Unit tests help to generate actual code which can be produced quickly using the existing codebase. Moreover, the test data could further be modified quickly to run the regression suite.

User Interface Testing

If it is any specific process where AI is shining, it is nothing else than user interface testing. AI-based user interface testing allows users to have the advantage of image recognition in the process of verifying the user interface while navigating through the application or software to check on multiple visual elements.

On top of that, AI test systems could aid exploratory testing where any glitches or bugs affecting the UI could be identified for verification. Moreover, the automated system can even manage to check on the layout, size, and color of the System Under Test. Thus, AI used for UI testing could offer expanded code coverage and help avoid even the minute UI deviations.

However, it is necessary that testers must be involved in the process to check the efficiency of the system for checking on various app versions, browsers, and platforms to ensure a similar outcome could be achieved throughout.

API Testing

API testing is one of the most complex procedures to follow since it needs testers to cover the deepest and widest range of scenarios to explore the effectiveness of the system. However, API test automation tools like SoapUI allow recording of the activities to analyze and create tests.

Normally, the automated tests need extensive modification and updates to delve into every parameter of the system that might affect the overall API suite. But the use of API test automation tools allows mitigating such challenges by quickly identifying all the connections and patterns between API calls. Also, the automated systems can even handle the changes by identifying them for rapid updating while producing dedicated scenario-based tests immediately.

In a nutshell, the novice testers who are not so informed of the way API testing works, the automation test tools designed to test APIs could help them to make things easy. Moreover, automation tools also aid change management through AI, which makes it much convenient to handle the task of API testing.

Automation Test Maintenance

Artificial Intelligence in the test maintenance process could help work on all the existing tests and with much greater precision when code change is not so typical. Either UI elements, field name, or test suites, Artificial Intelligence could help aid the test suite without causing any breaks to the process. Tools such as QMetry provide Self-healing feature that helps in identifying objects as the product evolves and necessary changes are made. Whenever there is a change in the properties, QMetry automatically detects these changes and suggests for an auto update of the changed properties. This helps in more stable test automation, reduces false positives, and reduces maintenance time.

Also, AI tools can even aid monitoring of the tests while working on the modified variants of the failed tests that align well with UI elements. Besides, automation-based test maintenance could even help yield greater test coverage and fix the gaps in the test system.

Test Data Generation

Automated Test data generation is another important aspect of artificial intelligence usage. The process involves the use of ML to generate data sets based on trained models that foster existing production datasets. The AI-generated test data is very similar to production data which simplifies the entire task of software testing, while the specific term which is used to denote the machine learning model that generates data is known as GAN or Generative Adversarial Network.

Some Tools That Support AI Test Automation using Machine Learning

As the entire process of leading automated tests is highly complex, taking advantage of some codeless and open-source tools could help complement the entire test process.

Concluding it all, Artificial intelligence has proven to be a significant tool that has helped to advance the existing testing practices. Right from the most basic Unit tests to the highly advanced API tests, the potential to yield AI through machine learning has enabled testers to unlock so many dimensions of the testing world. There are few tools such as QMetry Digital Quality Platform which is the first AI digital quality platform that offers test management, test automation, intelligent analytics and seamless integrations. Self-healing feature auto-updates locators when they are changed making your automation stable. At the same time, it provides QQBot in test management which automatically detects the duplicate test assets. The platform also has Wisdom module that helps in grouping of errors to separate false negatives from actual errors. Also, Wisdom enables advanced optimization recommendations that help you identify and resolve performance issues with automation suites like most recurring errors, most common failures.

Though some of the features of the technology are still on their way to maturity, the future of software testing holds so many things that are likely to be driven through the potential of artificial intelligence. Would this be the development of current tools to create more promising test suites or AI taking over the entire task of application development, testing, and release? It will be interesting to watch.

About Author: Kanika Vatsyayan is Vice-President Delivery and Operations at BugRaptors who oversees all the quality control and assurance strategies for client engagements. She loves to share her knowledge with others through blogging. Being a voracious blogger, she published countless informative blogs to educate the audience about automation and manual testing.

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