Artificial Intelligence (AI) was just a buzzword a few years ago, but now it has become an indispensable component of human lives. AI is present almost everywhere, from smartphones, smart devices, smart apps to self-driving cars. With AI used across all fields such as FinTech, Healthcare, Banking, Insurance, etc., certainly, this technology has its demanding presence even in the software testing field, which is at its peak today. Next-Gen technology is employed in test automation to complete the testing process smoother, faster, and more reliable.
An overview of AI-based software testing
The software testing market has gradually transformed from the initial manual testing to semi-automation, and then towards automation testing utilizing tools. Further codeless automation, automation utilizing bots leveraging AI & ML technologies, and particularly AI-based software test automation is in more demand in recent years.
AI-based software testing is an advanced software testing method in which next-gen technologies like AI, Machine Learning (ML), and Deep Learning (DL) are employed to test the software effectively. AI and ML use problem-solving and reasoning strategies to automate and enhance the testing process.
AI-based testing can be completed effectively by leveraging tools that employ data and algorithms to design and execute the tests without any human intervention. This advanced testing assures better test coverage, test accuracy, quicker time to market, and delivers a significant decrease in overall testing costs.
How can AI help overcome test automation challenges?
1. Discovering the right set of people:
One major challenge associated with automation testing is discovering the right team with the proper skill set for scripting.
Solution: Businesses can leverage AI-based test automation tools that deliver testers with a semi or completely codeless scripting platform, and can thus overcome the challenge of discovering the right team and skill set.
2. Time consumed in performing repetitive tasks:
Every time a new test automation project comes up, no matter how reusable the elements are, teams conclude up writing a lot of identical code again, which consumes a lot of time.
Solution: To overcome this challenge, AI can be used to generate test scripts quickly and automatically. AI tools can be prepared based on the last project inputs and outputs to develop test scripts automatically for similar projects.
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3. Flaky tests:
Testing teams spend hours deciding if the failed test is due to application issues or badly written test cases. These types of test failures are called flaky tests, due to which a release is unnecessarily held on hold, resulting in the pause of the software delivery.
Solution: AI can support teams to overcome the challenge of flaky tests by developing more robust test cases and discovering patterns in unexpected test failures to speed up the process.
4. Frequent script updates due to UI changes:
In order to deliver a seamless User Experience (UX), companies manage to modify the app User Interface (UI). Even though the transformation may be minor or unnoticeable, it could fail the test scripts while executing some actions on the page.
Solution: AI and ML algorithm-based tools can be prepared to resolve minor transformations in the code or bugs in the application. These tools can then take further steps accordingly and protect human efforts in script updates for such minor changes.
How is AI revolutionizing software test automation?
1. Helps automate the unit testing process:
AI can be utilized for static analysis of the software to determine which code area is not covered under unit tests. The AI-based software testing tools can employ this data to develop unit tests for uncovered code. These tools can develop unit tests and even update the tests whenever the source code varies.
2. Enables visual validation of UI testing process:
AI can be used for UI testing as it affects the use of image recognition procedures to guide through the application. It can demonstrate UI objects and elements visually to build UI tests. AI tools also utilize exploratory testing methods to discover bugs in the application’s UI. The AI tool develops the screenshot for later validation by QA engineers. It also confirms the other visual aspects of the system underneath tests like color, size, layout, etc.
3. Eases regression testing significantly:
The AI-powered tools and AI-powered engines help to build tests faster, run thousands of tests in one go, and greatly decrease overall test maintenance. These tests operate seamlessly across browsers and even mobile browsers.
4. Supports self-healing:
Many self-healing automation tools employ AI and ML to automatically update and adjust modifications in the UI or environment of the application. Generally, the AI tools detect the problems and remediate them without any human intervention through the self-healing process.
5. Delivers early defect predictions:
AI in testing helps in early and fast defect identification, diminishes defects, and makes the product bug-free and reliable for end-users.
Thus, it is clear that AI is used in all spheres of the automation testing method and ensures faster testing results more efficiently without any human intervention.
Leading AI tools for automation testing
UiPath Test Suite:
UiPath Test Suite merges the world’s leading Robotic Process Automation (RPA) technology with best-of-breed testing abilities to accelerate scaling through proactive testing, cross-enterprise partnership, and a consistent process to develop and deploy automation. UiPath Test Suite advantages practitioners who build automation and stakeholders who rely on automation, including transformation leaders, business users, consumers, and partners. UiPath Test Suite consists of Studio Pro, Orchestrator, Test Manager, and Robots, and other Test Studio Features. Moreover, it encourages collaboration through reusable artifacts and has a user-friendly interface.
It is an AI-powered, no-code quality automation platform that leverages the ability of AI to test mobile apps on a combination of android and iOS devices. It can be used to perform manual, automated, and RPA testing of apps. Uniquely, this no-code testing platform facilitates testing, saves testing time, improves productivity, and trims the testing life cycle. This intelligent automation testing tool facilitates manual, automated, and robotic testing in a meaningful way.
It is an enterprise software testing tool employed for end-to-end automation testing of software applications. This tool comes with great features like Vision AI, risk-based test optimization, model-based test automation, etc. The Vision AI feature of this tool leverages the ability of AI to automate and customize tests. It utilizes deep convolutional neural networks merged with advanced heuristics to deliver stable, self-healing UI automation. Vision AI, jointly with Tricentis Tosca, can be employed to automate even the hardest-to-access UI systems.
It is an AI-driven autonomous ongoing testing system that can be utilized for test creation and execution. This tool lets the generation of autonomous tests and codeless test creation and can be used to conduct functional, performance, and security testing. This tool supports continuous testing in a DevOps environment and can be smoothly integrated with Jenkins, TeamCity, CircleCI, Jira, and other CI/CD tools.
AI in software test automation has become a critical trend and has a tangible reality as AI has the potential to handle software testing to the next level. Certainly, it helps testers to generate more tests and provides the speed and reliability of automated tests. But incorporating AI into software test automation requires professional assistance as it is a complex procedure. Companies can leverage AI testing from a Next-Gen QA and independent software testing services provider for high-quality software and faster time-to-market.
At TestUnity, we strive for the highest quality in every project, and our professional QA specialists are ready to ensure it. Contact us if you’re looking for a dedicated team to enhance your product’s quality.
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