What is AI Testing?

AI testing is a type of software testing that uses artificial intelligence to enhance and streamline the testing process. The objective of AI testing is to evaluate a software’s capabilities, efficiency, and reliability by automating tasks such as test execution, data validation, and error identification. 

By leveraging AI capabilities, businesses can fast-track their testing process and improve the overall quality of their software.

Challenges in Traditional Test Automation

Traditional automation testing comes with a fair share of challenges ranging from sluggish test execution to the persistent issue of maintaining scripts. Listed below are some of the common challenges with traditional automated testing.

• Slow Test Execution: Slow test execution is the number one reason for delays in testing. Emphasis on UI automation, poorly designed test scripts, insufficient test case sequencing, lack of device coverage etc leads to slower test execution.
• Excessive Test Maintenance: Test scripts are sensitive to the app’s UI and structure. Therefore every small change in the UI requires changes in the test script. 40-60% of the total automation effort goes into script maintenance.
• Issues with Test Cases: Test cases can sometimes break due to minor changes in the code, such as renaming a component. 
• Test Data Generation: About 30% of automation issues arise from the complexities in managing and maintaining test data. Testers have to create test data generation scripts, use version control, etc to generate and maintain test data.
• Lack of Skilled Resources: Conventional automation testing is complex and code-intensive. As a result, nearly 50% of test automation projects fail due to inadequate planning and a lack of skilled resources.
• Slow ROI: A typical software testing automation platform is expected to break even after approximately 25 test automation runs, with a subsequent return on investment (ROI) of about 1.75 anticipated after around 50 runs. The intricacies of software testing economics become even more pronounced for organizations undergoing rapid digital transformation.

Why Should Companies Implement AI Testing in Their Processes?

AI testing differs from conventional software testing by leveraging AI for dynamic test case generation, self-healing test automation, intelligent test execution prioritization, and cognitive testing capabilities.

Unlike traditional automation, an AI testing tool will create test scripts using visual models, automatically adapt to application changes, identify potential defects, and automate complex tasks, thereby improving efficiency and coverage.

Listed below are a few reasons why companies should implement AI in Testing.

• No-code tests: Testers can easily automate tests using visual models such as record and playback, drag and drop mechanisms to create and execute tests without writing a single line of code. This completely eliminates the need to learn different frameworks and complex programming languages to execute tests, thereby allowing non-tech experts to get more involved in the testing process
• Smarter and faster test creation: With AI, Testers can create tests quickly by performing actions on screen. 
• Self-healing tests: The Self-healing capability dynamically updates test scripts to adapt to minor application changes, greatly reducing manual updates and maintenance of test scripts.
• Automatic Test Data Generation: Automates generation and maintenance of test data by intelligently understanding the prompt. 
• Easy scheduling, maintenance, and monitoring of tests: Automated scheduling and monitoring simplify test management, saving your business time and resources. With AI, you can schedule daily or weekly builds or intelligently integrate it with your CI system.
• Cost Reduction: When companies transition to intelligent codeless test automation, they typically see cost savings of 25% to 75%. This cost-efficiency is mainly due to reduced complex code maintenance and lesser reliance on specialized coding resources.

AI in Testing Automation – Methods

Several AI-driven methods in testing automation contribute to increased speed, accuracy, and adaptability in the testing process, including:

Self-Healing Automation

Self-healing automation uses AI to automatically update test scripts, saving time for manual intervention. It is beneficial when the application’s code changes frequently, leading to broken test scripts. This ensures that the test script remains accurate and up-to-date, reducing the time and effort required for maintenance.

Regression Automation

In Regression testing, testers often spend a lot of time to ensure that the functionality of the feature/software has not changed due to the introduction of new features. AI has revolutionized this process by intelligently automating tests based on code change. This not only accelerates the testing cycle but also enhances the overall efficiency and accuracy of regression testing, allowing teams to focus on more complex and creative aspects of software development.

Defect Analysis and Scheduling

AI-driven defect analysis uses machine learning to predict problem areas in the code. It prioritizes test cases based on the chance of defects, helping testing teams focus on critical areas. This ensures that testing efforts are optimized for efficiency, with the most important areas of the application tested thoroughly. Scheduling optimizes testing resources for efficiency, ensuring testing is done appropriately and with the right resources.

How Does BrowserStack Utilize AI to Simplify Testing Processes?

BrowserStack’s Low Code Test Automation offers the best-in-class features to run automation tests without writing a single line of code. Its intuitive and easy-to-use test recorder, captures user actions on the screen and translates them into automation tests.

1. Easy to use Recorder: Low Code Automation’s recorder records our actions and translates them into automation steps. This is a simple record-and-play mechanism and has no learning curve.
2. Auto Generation of Steps: It automatically captures all the test steps as the user performs the action on the screen. It can capture a wide range of user actions like clicking an element, hovering over elements, handling dropdowns, Keyboard actions like Key presses, managing iframes and shadow DOM elements, etc.
3. Test Data: BrowserStack’s AI has the capability to generate Test Data. This feature is crucial and eliminates the need to create and maintain different sheets for Test data.
4. Variables: Users can configure variables when they want to use a value multiple times during the test execution. This helps in eliminating hardcoded values, making the test more readable and self-explanatory and easy to maintain.
5. Self healing Mechanism: Low Code test automation, with its evolving self healing test automation capabilities, offers a sophisticated and proactive approach to automated testing. In the event of element discrepancies, such as changes in the UI, BrowserStack does not just report a failure. Instead, it actively seeks alternative identifiers or utilizes relative positioning strategies to locate the intended elements. This proactive problem-solving is a step towards self-healing, ensuring tests continue with minimal interruption.

Try BrowserStack’s AI capabilities and transform your testing process. Reduce manual efforts, and embrace change with confidence.

Try Low Code Automation now