We have already familiarized ourselves with artificial intelligence and machine learning and rely on these two technologies in QA automation. AI and ML can be integrated into automated testing processes.
Both these technologies are revolutionizing the world of QA automation. As we all know, automated software testing has evolved a lot over time. Earlier, it was functional testing, but today, there are several innovative automated testing tools. AI-based testing tools have been introduced lately.
Furthermore, there is a shift in the business approach that leveraging the endless potential of AI and ML technologies helps perform test automation more efficiently. In this article, we will look into the impact of these revolutionary technologies that shake the world of QA automation.
Why Should You Use AI and ML for QA Automation?
The answer is simple! AI and ML can be integrated into the automated testing process. It helps test scripts to be more competitive, efficient, and reliable. Furthermore, conventional automated testing tools pose several challenges. However, with the help of AI-enabled automation testing, you can ward off such drawbacks.
Machine learning in QA automation deals with using ML algorithms and methods to improve the key aspects of test automation, such as the generation of test cases, their execution, and the management of test data.
How does AI-enabled QA Automation Make a Difference?
Here are some areas where AI-enabled test automation brings great reliability and efficiency. Let’s go through each of the aspects.
Adaptability
Developers often make modifications to the application. Despite the changes being trivial, it can fail test scripts. At this point, AI and ML-enabled QA automation tools can lend a helping hand. AI and ML systems rely on their auto-healing methods to offset those changes to help the test scripts run smoothly.
Reduces Time
The testing team writes similar codes repeatedly when a new QA automation project comes up. It takes a lot of time while trying to adopt a new tool or adapt to the existing framework. It does not matter if it features code reusability, as it consumes time. However, with the help of AI automation tools, you can generate test scripts very fast.
Coverage
In the agile development process, running all the regression test cases after implementing each change is not always viable. But if you can use AI/ML tools, it helps you create regression test suites for the changes implemented so far. No matter the parameters, you can set the regression test cases in accordance with the project.
Error Detection
When you find it difficult to fix coding issues that affect the performance of your software application, AI-enabled automation testing comes to your rescue. AI in QA automation optimizes the error detection process, enhances testing methods, and makes predictions on possible failures. As a result, it directly translates into better customer satisfaction.
How Does ML Help You with QA Automation?
Let’s dive into the areas where ML facilitates QA automation.
Enhanced Test Case Generation
ML enables better analysis of patterns during tests and facilitates the generation of test cases. Therefore, manual test case creation is optional when you use ML in QA automation. As a result, it enhanced productivity. What’s more, it speeds up the process considerably.
Test Case Prioritization
Using ML algorithms in automated testing helps prioritize test cases based on the chances of getting bugs detected. It allows testers to consider the most crucial issues in the first place.
Better Test Coverage
As mentioned above, ML can recognize the areas where you are likely to find bugs. Further, it helps testers to pay attention to these areas and improve test coverage. ML helps you to extract the most relevant data.
Automating Repetitive Tasks
Do you need to automate test data generation and minimize the time, cost, and effort required for test execution? Fret not, using ML algorithms for test automation, you can reduce costs and automate repetitive tasks seamlessly.
Natural Language Processing
With the help of Natural Language Processing (NLP), you can automatically generate test case descriptions. Top of all, it helps better understand testing processes and test cases. It allows you to optimize and integrate the user experience with the delivery pipeline.
How are AI and ML Reshaping the QA Automation Landscape?
Today, the need to shift toward AI/ML tools in QA automation has become a necessity. With growing customer expectations and the need to keep up with the delivery pipeline, businesses have to depend on AI/ML-enabled test automation strategies. Let’s discuss a few ways in which AI/ML is transforming the big world of QA automation.
AI and ML enable the creation of manual tests and speed up the testing timeline by running test scripts automatically.
Predictive analytics will help understand the needs of users. Identifying their needs, in other words, makes way for better customer experience and satisfaction.
Visual validation in AI/ML helps identify defects compared to conventional software testing methods.
AI assists testers in covering more test cases and scenarios. Further, it helps them recognize defects due to enhanced coverage across the software application.
There is no room for assumptions when you use AI/ML automated testing tools. Typically, software testers make a lot of assumptions while they develop and implement test cases.
There are certain tools that implement comprehensive testing. Well, you don’t need to possess any prior knowledge of programming. Such tools make use of AI/ML to test applications.
Further, AI will create new roles and opportunities for QA engineers, which will help them apply the knowledge gained from AI. For example, how to apply the technology in software application testing. They can integrate their testing prowess with the AI experience to create and implement testing tasks. Also, they can use their skills along with AI to make full use of predictive analytics.
Automated software testing will continue to evolve during the coming years. Indeed, it necessitates continuous training on AI/ML technologies to meet the ever-changing needs of users.
What Role Does AI/ML Play in the Future of QA Automation?
The future is undoubtedly AI/ML-powered QA automation. As both technologies progress, we can witness several state-of-the-art testing tools along with the focus on enhanced automation and the highest levels of accuracy. Further, AI/ML-enabled QA automation makes way for ingenious and sturdy applications that reshape the digital landscape.
The integration of AI and ML in QA automation testing assists in improving software quality by leaps and bounds. Companies have to adopt these revolutionary technologies in test automation to ensure quality and embrace seamless possibilities in the test automation field.
Frequently Asked Questions
How does AI help QA automation?
Automated testing has changed the software testing landscape by accelerating the entire process. With the help of AI in testing tasks, you can reduce the need for manual testing.
How do you use ML in test automation?
Machine learning helps analyze patterns during tests and generate test cases automatically. Like AI-based test automation, integrating ML into testing helps minimize the need for manual test case creation. Further, ML algorithms improve testing speed and also increase productivity.
Does automation testing replace manual testing?
Automation testing brings in several advantages. However, you need manual and automation testing. Manual testing handles complex test cases. On the other hand, QA automation facilitates simple, repetitive tests. Therefore, automation testing cannot replace manual testing. You can use automation testing to make manual testing work more efficiently.
Get ThinkPalm’s QA Automation Services
Are you ready to integrate revolutionary solutions into our QA automation efforts? Then look no further than the expert solutions from ThinkPalm. We implement functional tests for agile/DevOps development using the shift-left approach for web and mobile devices.
We also provide QA automation services using CI/CD and stick to continuous testing. Our API services/microservices testing focuses on advanced data integrity and quality. We perform automated testing for large and complex data sets. For IoT, we implement and execute mobile, cloud, device, and end-to-end testing services.
We offer web applications, mobile applications, and datacom testing for several industry domains. Our testing services cover the entire product life cycle. We pay attention to continuous verification and quality assurance solutions to ensure enhanced software quality.
Let’s discuss your QA automation needs, the challenges you face, and your objectives, for we can suggest, implement, and execute the best test automation services using the right tool for you.
Author Bio
Silpa Sasidharan is a content writer and social media copywriting expert working at ThinkPalm Technologies, who aspires to create marketing texts for topics spanning from technology, automation and digital business solutions.
