Being one of the most critical phases of the software development life cycle (SDLC), software testing requires speed and accuracy to ensure fast delivery of the final product. Software testers need to quickly submit their feedback to the development groups and roll out bug-free updates in the minimum possible time to stay ahead of competitors.Given the required speed and complexity of software development projects, technology companies continuously search for resources to test intelligently and quickly. Those enterprise leaders who understand the importance of speed in software development strive to shrink the product delivery time without compromising on quality. In this context, AI-powered automated testing can be used to characterize the recognized controls while framing tools to reduce testing time and improve the software’s reliability.Given below are a few examples of integrating AI in software testing.
API testing is essential for a better user experience. With artificial intelligence, software testers can understand the patterns and relationships in different API calls, identify test parameters, and create a series of API calls. Based on the analysis of user behavior, advanced patterns and inputs can be produced for API testing with artificial intelligence. The generated tests improve the quality of the application user interface (API) and cover more edge cases.
Unit Test Generation
AI plays a key role in unit test generation during software testing. By performing the static analysis of the software, testers can use AI to identify the areas of code that remain uncovered and subsequently create unit tests. Besides, unit tests created by AI-assisted tools don’t need any setup or interactions and could be updated in the future every time the source code changes. Consequently, AI can help developers quickly maintain unit tests without spending so much time.
The AI-powered continuous testing process allows software testers to automatically detect the altered controls and observe even a single minute change. Users can identify the tests to get the greatest coverage with limited time by using risk-based automation. Likewise, the testers can integrate AI with test creation, execution, and data analysis to identify the controls, manually update the test cases, and effectively identify the connection between the defects and components.
AI-powered automated testing helps in the object application classification for UI. The categorization of recognized controls is performed during the creation of tools. This allows the software testers to pre-train controls and create a technical map such that the artificial intelligence is looking at the GUI to acquire labels.
Examples of AI-driven Testing
ApproachesFour primary examples of AI-driven software testing approaches include
1. Visual Testing
Allows users to compare screens and leverage image-based learning to test the application from an end-user’s perspective.Moreover, it’s particularly useful for catching issues that may not be apparent through traditional testing methods.
Suppose you have an e-commerce website, and you make a change to the checkout page. Visual testing would automatically detect and highlight any visual discrepancies, such as misaligned buttons or altered color schemes, ensuring the user interface remains consistent and visually appealing.
2. Self-healing Automation
Used to auto-correct element selection in tests whenever there is a change in the user interface (UI). It helps maintain the stability of automated tests in dynamic environments where UI elements may be modified or restructured.In a web application, if a button’s HTML attributes change due to a UI update, self-healing automation would intelligently identify and update the test script to interact with the modified button. This makes the automation suite robust and adaptable.
3. Differential Testing
Used to auto-correct element selection in tests whenever there is a change in the user interface (UI). Furthermore, differential testing involves comparing the output of two versions of an application or system to identify any functional or performance differences.
It’s valuable for detecting unintended changes introduced during updates or modifications.Consider a mobile app that undergoes an update. Differential testing would compare the output and functionality of the updated version with the previous one, highlighting any variances in behavior or appearance. This helps ensure that new features don’t involuntarily introduce lapses.
4. Declarative Testing
Helps the software testers to determine how to carry out the test and specify its intent in a natural or domain-specific language. This approach focuses on describing “what” the test should achieve rather than specifying “how” the test should be conducted.Instead of writing code to click buttons and input data, a declarative test might state, “The user should be able to successfully submit a form.”
This allows testers to express testing criteria in a more human-readable format, enhancing collaboration between developers and non-technical stakeholders.
Benefits of AI-powered Automated Testing
Here’s how integrating artificial intelligence in software testing can benefit companies.
Irrespective of their skills and expertise, humans are always vulnerable to error. Even highly qualified and experienced software testers make mistakes in manual testing. Sometimes, these mistakes could result in huge financial losses or badly impact the company’s brand reputation. To reduce the likelihood of errors in manual testing, artificial intelligence (AI) should be used during software development.AI-powered automated testing allows the testers to perform their process with accuracy and reliability. Software testers can free themselves from tedious manual testing steps and cope with complex features. This way, they can save additional time for innovation and record thorough outcomes.
In manual testing, software testers cannot implement a controlled web application test for multiple clients simultaneously. They have to run each test separately that increases the testing time as well as the chances of making a mistake. On a commercial scale, delayed product delivery time means disaster for a company’s reputation and competitiveness.With AI-powered software testing, QA engineers and testing professionals can reenact a large number of virtual sets of clients collaborating simultaneously with web-based applications. The technology helps software testers complete the same recurring tasks effectively again and again as per the project planning. By making artificial intelligence work on tedious tasks, the professionals can focus on exploratory tasks like R&D and the creation of effective automation solutions.
Increased Test Coverage
Innovation and diversity in the final product are key to success for tech companies in today’s highly competitive business environment. To improve the software quality, testers often search for resources to enhance the overall scope and depth of tests.With AI-powered automated testing, IT professionals get insights about program states, data tables, and file contents to investigate the behavior of the software product. They can perform thousands of distinctive tests with test automation and increase their test coverage that cannot be achieved with a manual testing process.
Artificial intelligence improves teamwork among the development team and encourages professionals to collaborate to find solutions to complex problems. Software developers, DevOps engineers, designers, and testers can use shared automated tests to timely identify the troubles and take steps to remove those errors. They can run tests automatically each time the source code changes and check whether it succeeded or not.Timely identification of bugs through teamwork can increase the developers’ confidence and significantly improve product quality.
In a manual process, companies have to repeat software testing each time source code is customized that is time-consuming and relatively more expensive. On the other hand, testers can implement AI-powered automated tests again and again without spending more time or resources.Consequently, they can significantly reduce and time of software testing and cut overall project costs without losing the software quality. By using AI testing tools, tech enterprises can release products on a daily or weekly basis and gain an economic edge over their competitors.
Faster Bugs Detection
In the context of AI-powered automated testing, visual testing plays a significant role. Machine learning algorithms can rapidly compare visual elements, identifying anomalies or inconsistencies that might indicate bugs.
Imagine an AI-powered testing tool analyzing screenshots of different UI states. It can quickly detect variations in layout, colors, or elements, helping testers pinpoint potential bugs faster than manual inspection. This accelerates the bug identification and resolution process.
Reduces the test-cycle time
Self-healing automation is a key component in reducing test-cycle time. AI algorithms can dynamically adjust test scripts when there are changes in the UI, eliminating the need for manual script modifications and minimizing downtime.
If a UI element’s attributes change due to an update, AI-powered self-healing mechanisms can automatically adapt the test scripts to interact with the modified UI.
Additionally, this agility reduces the time traditionally spent on script maintenance, allowing faster test execution and quicker feedback to development teams.
Encourages script execution
AI-driven approaches, such as declarative testing, make test scripts more accessible and readable. Natural language processing (NLP) capabilities enable testers to express test criteria in human-readable formats, encouraging wider participation in script execution.
With declarative testing, a tester can articulate testing requirements in plain language, such as “Verify that the login functionality works seamlessly.”
This simplicity encourages non-technical team members to understand and execute tests. Thus, generating collaboration and freeing up automation engineers for more complex tasks. The result is increased script execution across the testing team.
Artificial intelligence (AI) has been a buzzword in the global tech industry for years. The technology is being used in the software testing process to improve the product quality and increase time to market. Similarly, companies use AI-powered automated testing in item application categorization for user interfaces and characterize recognized controls.Additionally, artificially automated testing enables software testers to pre-train controls and make a technical map to get labels for the particular controls. This way, using AI in tedious software testing can improve software’s accuracy, increase testing speed and coverage, encourage teamwork, and reduce overall project costs.