Artificial intelligence (AI) and machine learning (ML) have made deeper inroads in several business verticals. As the technology is becoming mature, it is now also used for more business-critical scenarios where it can enhance efficiency, reduce human errors, decrease costs, and predict information and cases to benefit businesses in the future.
As a result, QA and software testing services have now also started leveraging AI and ML to make the entire process more feasible, efficient, and cost-friendly for businesses.
What are AI and ML in QA?
The application of artificial intelligence and machine learning in quality assurance (QA) and software testing is leveraged to enhance the efficiency of the software development lifecycle (SDLC). Through problem-solving, reasoning, and machine learning, AI is leveraged to automate and cut down the time taken for redundant and tedious tasks.
Well, test automation tools already have the capacity to automate tests but they have certain limitations. Here, AI is used to fill those gaps and eliminate limitations. For instance, the majority of automation tools can be used to schedule, run and publish tests. However, they are not capable of determining the type of test to run. They can either run some predetermined tests or the entire suite of tests.
In such cases, AI can review the current status of code coverage, test status, recent code changes, and more to identify the most suitable test to run. Hence, it can help in making data-driven decisions to identify the right test coverage. AI and ML have brought a new dimension to the end-to-end QA process.
Let us check out how QA and software test automation is evolving at a rapid pace with changing industry demands and technologies.
QA is Getting More Complex
Amid increasing technical complexities and cutthroat market competition, the need to roll out reliable and performance-oriented applications has increased exponentially. Hence, the need to execute software testing in more smarter and efficient ways has also increased. This has increased the challenges for QA and software testing teams manifold.
How AI and ML can Help Solve QA Challenges?
With the current practice of DevOps, continuous testing, and agile; the software development process has become significantly faster. Hence, to unlock the real potential of software testing, it is crucial to leverage AI and ML.
Here is how AI and ML can help:
The AI-powered tool is used for monitoring and visual testing. It is capable of running tests on multiple platforms and browsers. It leverages AI to find out impactful changes in UI and mark them as enhancements or bugs. It can also leverage ML or AI to run automated maintenance tasks.
It is capable of automatically detecting the changes occurring in the elements of your application. To compensate for those changes, also dynamically updates the changes. All you have to do is just show the workflow that needs to be tested.
It is a cloud-based automation testing platform. It leverages AI and ML to analyze code base and run tests that could cover the bugged area. Its intuitive dashboard reflects analyzed results and also offers continuous test management.
It works like building as a tool that adds an AI brain to Appium and Selenium. A simple format is used to define tests including the BDD syntax of Cucumber. Hence, no code is required and also you don’t need to handle element identifiers.
It is capable of generating test scripts on the basis of data generated by real users. You can leverage these scripts for performance and functional testing.
While we discuss the possibilities of implementing AI and ML into the real world of QA and software testing, it is also crucial to discuss all the potential challenges that businesses could face during the process.
Let us find out:
QA and software testing have come a long way from the linear waterfall model to agile and DevOps. And as we step into working with futuristic technologies and advanced workflows, we need to leverage AI and ML to stay aligned with fast-paced SDLC. Hence, the quality assurance domain is poised to witness substantial use of AI and ML in the near future.
AI and ML will help QA teams to achieve greater accuracy, generate more revenue, and cut down the cost of multiple QA processes. Hence, it will help in making your business more competitive while enhancing the customer experience. Now, test engineers need to research more on AI and ML, leverage AI-enabled tools and figure out efficient ways to leverage them in the day-to-day process.