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You might have heard the words AI and ML everywhere for many years. Whether it is about serving customers, building products, or testing any application, AI ML is present in every part of
technology. If you are a QA engineer who is looking to make the most out of this technology, you will first have to understand what this technology is and what are the applications of AI and
ML in software testing. Once you understand their applications, you are good for using Artificial Intelligence for software testing in all your projects. So, without waiting any further, let’s understand what AI is.

What is AI and ML?
Artificial Intelligence and Machine Learning is a set of revolutionary technologies that can help you develop human-like capabilities in machines and computers. With ML, machines can learn
from data and perform tasks on their own for a predefined problem. On the other hand, AI is the technology that helps machines mimic intelligent behaviors that can be used in solving ambiguous problems and things that are hard for humans to do on their own. Both these technologies are used by Software testing companies to expedite the software testing process at a large scale. Suppose you have hundreds of pages to test and report the findings; if you have only one QA engineer, it will take a lot of time. But if you have an AI/ML model for this testing, you can do the entire testing in minutes with superb accuracy. By now, you are aware of these technologies, so let’s have a look at their applications in the context of software testing. Applications of AI and ML in Software Testing AI-backed Test Data Management Test data management is a crucial process in any software testing endeavor. When you are testing the software, you also need to generate synthetic data or use data in a way that no privacy policies are broken. While this can be hard, AI makes it significantly easier. AI can help you generate synthetic test data for your testing process so you don’t have to use real-world user data, and this ensures that you never violate any privacy policies. If you use real-world data, AI and ML models can help you cover the real data and anonymize the records in a way that nothing is readable to humans and privacy is never violated. If you are using the same test data again and again, AI can also help you find a set from your test data with enough variation so that the system can be tested from all angles.

Automatic Environment Management
Managing test environments and replicating the environments from production systems is crucial to ensure the correct results of any testing process. Many times, software testers spend hours configuring their testing environments so that they are similar to production systems, but this can be made easier.

With AI, you can set up testing environments in a snap and start testing the software as quickly as possible. Moreover, AI can learn from past environment setups and help you in identifying
the correct resource provisioning strategies. This can save costs significantly both in the test and production environment. Besides this, AI can also keep a close eye on the test environment
and monitor it to detect any anomalies or performance spikes that can be reported later.

Intelligent Defect Prediction
Everyone knows that AI is many folds more efficient than a normal human being, and this characteristic comes to the rescue when dealing with defects. When you do software testing with AI models, you can predict defects even before they take place, and the model can also suggest remedial actions that can help you avoid the defects. Intelligent defect prediction is done through AI models that keep monitoring the application’s performance and learn the baseline performance levels of the application. Once they are able to identify a baseline, whenever there is some unusual activity in the application, it can be flagged without the need for humans. The AI model can flag the issue, which can be later fixed by humans, or it can also work on its own to fix the defect. The best part about such AI models is that the accuracy of your testing results is much higher, as no bug goes unnoticed.

AI-Powered Test Automation Frameworks
There are many automated testing frameworks where AI is used heavily, and the framework can help you design and run better test suites. With such test automation frameworks, manual
testing engineers don’t have to write entire code for every test. Rather, the framework will provide them with useful suggestions that can help in writing multiple test cases. Apart from suggesting the code, AI-powered test automation frameworks can run many tests parallelly, and they can also report the findings more accurately than humans. If you have to test your applications at a large scale, you have to use AI in testing. This will help you get your work done way faster than a dedicated testing team working on it.

Benefits of Using AI ML In Software Testing

Accurate Load and Performance Testing
Load and performance testing is a part where using AI ML makes a huge difference. In such testing processes, you need different types of requests, and they need to come at a rapid pace
so that the system can experience load. AI models can create different types of requests and send thousands of them across your app to test the app for its performance under heavy loads.

Root Cause Analysis
Finding the root cause of any issue is hard, and that is where AI-based software testing helps. The AI model can help you find the root cause of the problem in your codebase by understanding the codebase, reading through the logs, and establishing relations between interactions in the codebase.

Decreased Errors
It is widely known that AI and ML models are quite good at what they do. Software testing is not different at all. When you do software testing with the help of AI models, you can definitely have lower errors in production as everything will be reported upfront, and you can fix them before it becomes an issue.

Coming to an end, the future of QA is bright if you adopt AI and Machine learning in your processes. While these technologies are smart, they may never replace Software Testers, and both QA engineers and AI ML technologies will work in harmony to provide an awesome and bug-free product to end users.



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