The simulation of human intelligence processes by machines, particularly computer systems, is known as artificial intelligence, or AI. This broad category includes all methods, tools, and software that let computers carry out operations that normally call for human intelligence. Identifying patterns, solving issues, making judgements, and gaining knowledge from past experiences are some of these tasks. In this blog post, we will dive into the challenges that AI addresses.

Finding an element visually:

The use of locators in dynamic applications may lead to unstable test scripts, which will make it difficult to maintain regression tests. This problem can be perfectly solved by automating software tests using artificial intelligence. AI can recognize elements using visual cues, just like a human user, rather than depending on DOM properties (e.g., icons such as My Account, play, pause, mute, cart, and settings). AI in test automation can seamlessly execute user actions by specifying the element itself, instead of providing locators based on HTML DOM properties.

Test scripts that can self-heal:

Development teams continuously add new features to provide a flawless user experience. Regression testing must therefore be done by QA teams on a weekly, monthly, or annual basis. Though it negates all the advantages of test automation, test script maintenance remains the largest challenge. Instead of failing the test script, AI-driven test automation tools update the scripts automatically, saving human intervention, and can quickly detect changes in the user interface.

Automated generation of test procedures:

Test script authoring can be greatly simplified by AI-powered test automation platforms, which allow non-technical users to directly input test cases in English. By reading the test steps, AI tools that leverage natural language processing (NLP) can produce automation scripts. Generative AI can decipher user intent and replicate those actions on software programs. As a result, testing teams can automate workflows without writing any code. It saves more than 70% of the time and effort required to design test scripts.

Smart regressions:

Risk coverage is a problem that AI software testing addresses. When determining the regression test suite size, AI-based tools consider more parameters than are feasible for humans to handle. QA teams can use this to run only the affected test cases, as opposed to the full regression suite. This will largely save time.

Synchronization and simulation of workloads:

AI can create workload scenarios that are realistic and analyze historical user behavior data. Automated tests that fill in any gaps can be created, and traffic patterns that closely mimic real-world usage can be simulated. To obtain more accurate performance test results, AI can more precisely synchronize the actions of virtual users.

In conclusion, Opkey is a testing automation platform driven by AI that streamlines the testing process. Test cases that are affected are automatically identified by its AI-based change impact assessment, which also suggests test cases based on the highlighted risk. By doing this, QA teams can save time and improve coverage by avoiding the needless execution of unnecessary tests and only executing pertinent test cases. Test script maintenance is made simple with Opkey, which automatically detects malfunctioning scripts and fixes them without the need for human intervention.