AI in Test Automation: Benefits, Methods & Tools

In this article, we'll discuss the role of artificial intelligence in software test automation and guide how to use AI in your testing effectively.

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The newest trend in quality control is the use of AI in test automation.

Automating the testing procedure and quality assurance testing results in considerable time and cost savings. And now, a growing number of businesses are relying on AI and ML for test automation.

It's just a matter of time until AI significantly changes the testing process. So how is AI revolutionizing software test automation?

In this article, we'll discuss the role of artificial intelligence in test automation and guide how to use AI in your testing effectively.

An Overview of AI-based Software Testing

It is a vital step that assures the application's customer satisfaction. Test automation is the methodical process of observing an application under controlled settings to ascertain whether or not certain risks associated with software development are acceptable.

The use of artificial intelligence in software testing can help protect an app against future failures that might be destructive to the app and the company. Since AI is becoming more prevalent, it is important to put it through its paces through testing.

For example, if the intelligence in self-driving cars malfunction and it makes a poor decision or the reaction time is slow, it might easily cause a car accident, putting human lives at risk.

Is Automation and AI The Same?

Automation and artificial intelligence are frequently used interchangeably because they pursue similar goals: allowing more intelligent and effective business operations. However, AI and Automation is not the same.

Automation is the process of programming robots to perform a set of predefined rules, while AI is about programming machines to make their own decisions based on human input.

So what are the differences between Automation and Artificial Intelligence?

	Artificial Intelligence	Automation
1	AI evaluates all available data and uses that knowledge to make a decision.	Automation is similar to pre-programmed and self-running to carry out specific activities.
2	Understands data	Collects data
3	AI interacts with people, learns from experience, compares circumstances, and adapts its behavior accordingly.	Automation does not interact with people and operates solely on instructions.
4	For non-repetitive tasks	For repetitive tasks based on instructions and rules
5	AI is a technology that assists professionals in analyzing events and making decisions.	Automation is a kind of machine designed to do repetitive tasks.

We suggest you read our blog: Test Automation Strategy: Importance, Benefits & Example.

How Can AI Help Overcome Test Automation Challenges?

It's evident that not all of the many AI-powered testing products focus on the same challenges or the same points in the testing lifecycle.

All approaches and methods for testing AI have one thing: the need for more and better-automated testing. To do this, AI technologies will reduce or eliminate obstacles that need innovative testing strategies.

Let's look at how AI testing could overcome those challenges.

Slow Test Development Cycle

A slow test suite is hated by everybody. There are numerous ways in which AI testing tools might help solve this issue.

They can help you better manage your test data, which will speed up the delivery of high-quality information to your test cases.
Through this, they can determine how few tests are necessary after a certain code change, speeding up the CI/CD workflow.
Last but not least, they could be able to spot unnecessary test cases before they're implemented.

Extreme Test Sustenance

Web apps can change all the time. Fragile tests can break easily if the code is changed, especially if the identifiers for the element are on the page.

This situation may include changing the CSS class of a button, rendering the software testing tool incapable of detecting the component.

An AI-driven solution can overcome this problem by using ML methods to develop a better placement strategy for page items, resulting in more rigorous testing.

Creation of High-quality Test Cases

Creating useful test cases is a challenging task. Artificial intelligence may help by using testing tools to generate unit and API-based test cases.

Better tests can be generated with the help of test automation tools, which can identify reusable pieces that can be called instead of duplicated.

Wondering How to Create Test Cases for Automated Tests?

Extensive QA and Testing Efforts

Briefly, we have considered devoting more time and resources to testing and quality assurance. There are likely test cases within a large test suite that aren't strictly necessary since they duplicate the work of other, more important test cases.

They might not make a big difference, but they can add up when testing is implemented. An artificial intelligence testing tool could be able to detect and eliminate unnecessary work while running test cases.

In addition, similar comparisons might be conducted proactively while developing, warning developers when they are about to adopt a potentially harmful test case. However, with automation, all parties can focus on other equally important matters.

Early Detection and Elimination of Defects

New approaches to testing using artificial intelligence improve test quality by identifying and removing bugs earlier in the software development life cycle. Artificial intelligence and related fields, such as Data Science, have a major effect on the practice of doing early and healthy testing.

Low Test Coverage

Test coverage measures how many of your application's features, project requirements, and key risk points have been tested.

We could also benefit from AI-guided testing tools in this respect. Using data from previous exploratory testing sessions, an AI program might manage and produce new test cases to guarantee comprehensive coverage.

Combined with a risk-based strategy, in which the tool inspects app metrics to regulate two functions, this would be overwhelmingly helpful.

The first is identifying the most vulnerable parts of the application and, from among them, the most potentially disastrous in the event of a failure.

How Is AI Revolutionizing Software Test Automation?

Visual Verification and Testing

The major flaw in using automation tools like Selenium is that they only function based on locator strategy, which means that they only replicate human action on object locators and do not check the location and display of objects in the UI. Despite their popularity, however, these instruments do not provide the further step of checking the program visually.

Algorithms based on artificial intelligence have great potential to fill this need. The AI subfield of machine learning employs various patterns for the visual verification and identification of items. Despite ML's potential, few market products include visual testing into default automation frameworks.

Visual validation solutions based on machine learning might help you spot discrepancies that a more generic automation tool would overlook. This is useful for confirming the app's operation and establishing that it looks good.

No-code Test Automation

Although test automation reduces the time spent on testing by humans, everyone is aware that fully automating a web application may take months or even years.

To reduce the time and resources spent on test automation, companies are attempting to create codeless test automation systems that can read and execute business processes defined in plain English. This has the potential to reduce the workload for automation engineers significantly.

Web Crawling/Spidering

Manually developing the automation framework and then manually going through all the web pages to identify the UI components for the building of object repositories and action methods is the common practice in test automation. In addition, an automation team needs months or even years to fully automate a web application because of the effort spent developing test scripts based on business logic. However, many new tools are appearing on the market that use the power of AI to automatically scan the application and build the test scripts within a week, which is a huge time and effort saver.

Robust Element Locators

Despite the benefits that new UI technologies have brought to UI developers, test automation engineers are finding the dynamic and content-rich web pages produced by web development frameworks to be more of a burden than a help. Too many changeable IDs and HTML DOM attributes complicate the engineer's work. This highlights the need for a reliable test script that clearly specifies a powerful web element locator. Most automated regression suites' tests fail when the wished-for UI components are not at the expected locations.

However, thanks to AI, test automation engineers have less stressful and more reliable work. The use of a single web element locator developed by automation engineers may be improved upon by training an AI model on a huge dataset collected from several websites.

Automatic Defect Assignment And Triaging

It takes the agile development team a long time to go through the defects identified by the QA team and assign them to specific sprints once each sprint ends. Also, every development team must do this duty since they must guarantee that critical bugs will be handled in the next sprint.

In this case, artificial intelligence can significantly affect how defects are automatically ranked and assigned. An AI engine can use a defect's severity and the feature to categorize and prioritize it automatically. The engine can also help a developer identify new flaws based on existing ones. Also, similar bugs in the same feature can be given to the same developer so that all of the bugs can be fixed at once. This can save a lot of time and work for development teams.

Maintaining Test Suites And Test Scripts

As an app grows, it gets harder to keep track of many test scripts. AI tools can be used to keep track of and pull out good test scripts based on testing needs. This means that AI can be used to solve this problem. So, AI is expected to help solve the problems with traditional test automation and bring about a revolution in test automation.

AI-based Software Test Automation Methods

Leading AI Tools For Automation Testing

Tosca Tricentis

It is a business solution used for end-to-end automated testing of software applications. This solution has amazing features like Vision AI, model-based test automation, risk-based test optimization, etc. The Vision AI component of the solution automates and personalizes the testing process with artificial intelligence. It uses deep convolutional neural networks and smart heuristics to make UI automation reliable, self-healing and works on any platform. Vision AI can be used with Tricentis Tosca to automate even the hardest-to-use UI systems.

UI Path Test Suite

UiPath Test Suite combines the best RPA technology in the world with the best testing tools to speed up scaling. This is done through proactive testing, cross-enterprise collaboration, and a uniform way to create and deploy automation. UiPath Test Suite benefits those in the automation industry and those who depend on automation, such as transformational leaders, business users, customers, and partners. Test Manager, Studio Pro, Orchestrator, Robots, and other Test Studio Components are part of the UiPath Test Suite. It also h

Mabl

Intelligent test automation platform optimized for continuous integration and continuous delivery. Mabl will scan your app's screens and immediately begin running the standard, industry-standard tests. Further, it employs ML methods to enhance test execution and fault identification.

Sofy.AI

It is an AI-powered quality automation tool that doesn't require you to write any code. AI is used to test mobile apps on various Android and iOS devices. It can be used to test apps by hand, automatically, or with RPA. This one-of-a-kind no-code testing platform streamlines the testing process, shortens the testing life cycle, and increases productivity. This smart automation testing solution makes manual, automatic, and robotic testing much easier.

Want to learn more about Top 20 automation testing tools.

Benefits of AI in Test Automation

The Future of AI in Test Automation

There is no limit to what can be done in the world of artificial intelligence, and each year sees a higher level of sophistication in using AI algorithms to carry out intelligent test automation.

So, even though many AI test automation tools are still in their early stages, it is clear that AI has a lot of potential to change the way software testing is done in the future.

Wrapping It Up!

In the last two decades, there has been a dramatic shift in the software testing paradigm. The testing process has progressed positively from manual testing to automation testing, with selenium being one of the best test automation technologies. In today's fast-paced IT world, however, software testing requires new and well-researched testing approaches. For this reason, AI-based testing has come onto the scene and has been very helpful.

AI algorithms can act like humans, and ML lets computers learn independently without help from a person. AI is interesting because it involves making unique and specific algorithms. These algorithms can access data, learn from that data by finding patterns, and then use those patterns to make decisions. The best way to test software with these predictions is to use them.

Also, businesses are rushing to find tools that can use AI algorithms and can be used to test software well. AI-based testing has also been shown to help businesses in many ways, such as making testing faster and more continuous, automating everything without human help, and getting a quicker return on investment.

Trying to improve your software testing? Check out Ronwell’s Software Testing Services to learn more about how we can help your company succeed using cutting-edge technology.