How is AI Changing the World of Testing?

March 21, 2022 by shilpa.ks

Automated AI Testing

Artificial Intelligence (AI) was only a fad a few years ago, but it has now evolved into an integral aspect of human life. From smartphones to smart devices (Alexa, Siri, etc.) to smart apps (Cortana, Google Assistant, etc.) to self-driving automobiles, AI is nearly everywhere. With AI being used in a variety of industries such as healthcare, banking, fintech, insurance, and so on, it’s no surprise that this technology has a strong presence in the software testing industry, which is on the rise today. In test automation, next-generation technology is utilized to make the testing process smoother, faster, and more dependable.

Introduction to Artificial Intelligence (AI)-based software testing

The software testing market has gradually evolved from manual testing through semi-automation, and finally to tool-based automation testing. During these days, there is an increase in demand for codeless automation, automation employing bots leveraging AI & ML technologies, and notably AI-based software automation testing.

AI-based software testing is an advanced software testing technique that employs next-generation technologies such as artificial intelligence (AI), machine learning (ML), and deep learning (DL) to efficiently test software. To automate and improve the testing process, AI and ML use problem-solving and reasoning approaches. AI-based testing can be done effectively with the help of tools that use data and algorithms to develop and run tests without the need for human interaction. Better test coverage, test accuracy, and a shorter time to market are all benefits of advanced testing.

How can AI assist in overcoming test automation challenges?

Identifying the right people: Finding the right team with the right skill set for scripting is a major challenge in automation testing. This can be overcome by using AI-powered test automation tools to provide testers with a semi- or fully codeless scripting platform, overcoming the challenge of finding the right team and skill set.

Time spent doing the Recurring tasks: No matter how reusable the components are, every time a new test automation project arises, teams find themselves creating a lot of comparable code again, which wastes a lot of time. To address this issue, artificial intelligence (AI) can be used to develop test scripts fast and autonomously. AI tools can be trained to develop test scripts for comparable projects based on past project inputs and outcomes.

Tests that are unreliable: Testers spend a lot of time trying to check whether the failure of the test can be caused by application bugs or poorly written test cases. Flaky tests are the type of test failures that cause a release to be put on hold unnecessarily, causing software delivery to be delayed. To speed up the process, AI can assist teams solve the difficulty of flaky tests by designing more robust test cases and identifying trends in random test failures.

UI changes necessitate frequent script updates: Businesses frequently change the app User Interface in order to provide a consistent User Experience (UX) (UI). Even if the change is slight or undetectable, it may cause the test scripts to fail while executing certain operations on the page. Algorithm-based AI and machine learning tools can be trained to detect tiny code modifications or problems in an application. These technologies can then take further actions as needed, saving humans time and effort in updating scripts for minor modifications.

Choosing the right test size and test cases: Regression testing must be performed every time an application change is made. However, in a fast-paced setting, running the whole regression suite of test cases for each change is logically impossible. As a result, finding the appropriate size and number of test cases to execute for a given modification has always been difficult for testing teams. Artificial intelligence (AI) can assist testers in developing algorithms that estimate the correct size and create the regression test suite for a particular modification depending on various characteristics.

Traceability of test suites and test scripts: Maintaining a large number of test scripts becomes more difficult as the size of an application grows.AI technologies can be utilized to maintain and extract the appropriate test scripts as per testing needs, which can be used to tackle this difficulty .As a result, AI is considered to be bringing a revolution in the field of test automation by helping to overcome the challenges of traditional test automation.

What role does AI play in software test automation?

Aids in the automation of unit testing: AI can be used to perform a static analysis of software to determine which code areas are not covered by unit tests. This information can be used by AI-based software testing tools to construct unit tests for undiscovered code. When the source code changes, these tools may build unit tests and even update them.

Allows for visual validation of the UI testing process: AI can be used for UI testing because it employs image recognition techniques to navigate through the application. To create UI tests, it can visually verify UI objects and elements. AI tools also employ exploratory testing techniques to detect bugs in the application’s user interface. The AI tool creates the screenshot, which is then verified by QA engineers. It also validates the other visual aspects of the system under test, such as color, size, layout, and so on.

Significantly reduces overall test maintenance: AI-powered tools and engines help to create tests much faster, and help running thousands of tests at a time, and significantly reduce overall test maintenance. These tests run smoothly in all browsers, including mobile browsers.

Aids in self-healing: AI and machine learning are used by many self-healing automation technologies to automatically update and modify changes in the application’s UI or environment. Typically, AI programs notice faults and use the self-healing method to resolve them without the need for human interaction.
Provides early defect predictions: AI in testing aids in the early and rapid detection of bugs, the reduction of defects, and the creation of bug-free and reliable products for end users.

Methods for automating software testing based on artificial intelligence

Automation of regression suites: Regression testing consumes a lot of time and energy from the tester. Regression suite automation with AI conducts automated tests intelligently based on changes in the code each time. The main goal of this test is to reduce the time it takes to run a regression test cycle by identifying and running the proper set of test cases.

AI-based testing is a sophisticated software testing strategy in which AI, machine learning, and deep learning technologies are combined with the automation testing process to improve software quality and speed up delivery.

Defect analysis and prediction entails the use of ML and NLP algorithms to aid in the effective identification of software defects. The primary goal of this test is to ensure the early detection of defects and to assist businesses in entering the market as soon as possible.

Self-healing automation: An AI-enabled self-healing process involves the automatic repair of test automation script breaks that may occur as a result of object or other property changes. This test’s primary goal is to ensure less manual intervention and a faster self-healing process.

Top Artificial Intelligence (AI) testing tools

Parasoft is an automated testing tool that may be used to automate tests at every level of the software development process, from code analysis through user interface testing. It’s a test suite with a variety of components, and it’s recently included AI and machine learning to software test automation to assist enterprises in adopting static analysis. The AI solution from Parasoft may be used to detect errors early in the development process and verify compliance with a number of functional safety, security, and coding requirements. It relieves team members of their responsibilities and guarantees that severe design issues are handled first.

Functionize is a cloud-based automation testing tool for functional and non-functional testing (load and performance testing). This tool accelerates test creation and maintenance by utilizing AI and ML algorithms. To create a functional test, this AI tool employs plain English and NLP. Furthermore, this tool can run thousands of tests in a single minute from all desktop and mobile browsers.

Applitools is a visual testing and monitoring platform powered by AI that is simple to set up and integrates with all modern test frameworks. This tool supports all major browser versions and allows you to test the app’s functionality and appearance on a variety of devices such as mobile phones, tablets, laptops, and external monitors.

UiPath Test Suite combines the world’s leading RPA technology with stylish-of- strain testing capabilities to expedite scaling through visionary testing,cross-enterprise cooperation, and an invariant approach to design and emplace robotization. Interpreters who produce robotization and stakeholders who calculate on robotization, similar as metamorphosis leaders, business druggies, guests, and mates, benefit from the UiPath Test Suite. Test Manager, Studio Pro, Orchestrator, Robots, and other Test Studio Components make up the UiPath Test Suite. It also offers a stoner-friendly UI and encourages collaboration through applicable vestiges.

Sofy.Ai is a no-code, AI-powered quality automation tool that uses AI to test mobile apps on a number of Android and iOS devices. It can be used to test apps manually, automatically, or with RPA. This no-code testing platform is unique in that it simplifies testing, saves time, boosts productivity, and reduces the testing life cycle. This clever automation testing solution makes manual, automatic, and robotic testing more easier.

Tricentis Tosca is an enterprise software testing platform for end-to-end software application automation testing. Vision AI, model-based test automation, risk-based test optimization, and other capabilities are included in this solution. This tool’s Vision AI function uses artificial intelligence to automate and customize testing. It combines deep convolutional neural networks with smart heuristics to provide reliable, self-healing, platform-agnostic UI automation. With the help of Vision AI and Tricentis Tosca, even the most difficult-to-use UI systems can be automated.

Conclusion

As AI has the potential to take software testing to the next level, it has become an important trend and a tangible reality in software test automation. It undoubtedly assists testers in producing more tests while also ensuring the speed and reliability of automated tests. However, because integrating AI into software test automation is a complex process, professional assistance is required. AI testing from a Next-Gen QA and independent software testing services provider can help businesses achieve high-quality software and a faster time-to-market.