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AI-based automation testing helps the testing processes to achieve a slew of benefits. These include automated building and maintenance of test suites, predictive self-healing, visual validation, and cloud-based virtualization. 

The software development lifecycle is becoming more complex and tech-driven to meet the requirements of the quick release of quality software applications. Since software testing is a critical component of the whole process, it needs to be made faster, smarter, and better. The best way to achieve the same is to employ a smart AI testing strategy wherein QA specialists can move towards precision testing on a continuous basis. By leveraging machine learning, problem-solving, and reasoning, AI-based automation testing can reduce the quantum of tedious and mundane tasks in the SDLC. The key role of AI-driven testing is in reducing the direct involvement of QA specialists and developers in conducting routine activities. Normal test frameworks cannot analyze and decide the type of tests to be conducted, leaving it to the discretion of the testers. 

It results in the testers running all test cases or a few of the scheduled ones thereby leaving testing at a sub-optimal level. An AI testing framework, on the other hand, can analyze metrics such as test status, code coverage, and changes, among others to decide on the type of tests to be run. In other words, Artificial Intelligence can carry out decision making without the help of the developer or tester. The best examples of using AI in digital technology are the smart chatbots or voice recognition software like Siri (Apple) and Alexa (Amazon). These two are routinely used by people to browse the web and look for information, find directions, book appointments, and manage other activities. Siri and Alexa leverage machine learning algorithms to analyze past data patterns to predict customers’ requests, understand their habits and preferences, and make recommendations. Alexa can even connect with the smart thermostat called Nest to automatically adjust the room temperature by using the behavioral data of the homeowner.

How can AI-based testing impact the overall testing scenario

The need to provide the best quality products quickly into the hands of the consumers has driven software enterprises to adopt new tools and practices. Robotics Process Automation (RPA) using AI is shaping business priorities to achieve new thresholds of quality, speed, and cost-effectiveness. As test automation remains an underutilized practice in most enterprises, AI-based automation testing can truly fulfill the testing requirements delineated in Agile and DevOps methodologies.

Building test suites: AI in software testing can simulate the behavioral patterns of users based on geography and devices, and use them as inputs to build smart test suites. Further, it can analyze data from the social media interactions of users, mine defects, and make intelligent decisions on test coverage and optimization of test suites, rapidly and consistently.

Maintenance of test tools: The task of maintaining test cases is tedious because of the complex architecture and workflows of the test cases. Also, maintaining and executing automated testing tools still require manual intervention to some extent, which can be eliminated through AI automation testing. The time and effort saved by the testers can be put to good use by focusing more on exploratory testing.

Predictive self-healing: Changes made to software applications are a recurring feature, which often results in the breaking of UI tests as objects are not found. This is a major challenge faced by QA specialists who not only have to conduct tests but maintain the test suites and object repositories as well. An AI testing framework can dynamically update the test suites when the software application undergoes changes. Hence, QA specialists can cut down their time and effort on maintaining the test suites.

Visual validation: A robust AI testing strategy involving pattern and image recognition can detect visual bugs in the software application. This is done by ensuring the visual elements appear and function as expected. In fact, AI can identify and analyze the dynamic UI controls on a pixel level. 

Cloud-based virtualization: Any software application needs to be tested on a plethora of devices, their screen sizes and resolution, operating systems and their versions, and browsers and their versions, among others. These have led to increasing the complexity of test environments wherein testing teams have to filter and execute UI testing. No wonder, managing such test environments has become cumbersome, time-consuming, and expensive. The Artificial Intelligence led testing can analyze historical test results and user data to help teams virtualize the specific test environments on the cloud.

Conclusion

With the rising complexities of software applications, device platforms, and test environments, AI test automation is the way forward for enterprises to stay competitive. It helps QA specialists to achieve quality control, stability, maintainability, and scalability of the test suites.