Still not using AI testing in your software development cycle? We are sorry to say, but you are massively falling behind considering the current standard of the app development and testing industry. This is because modern applications have reached such a complexity that it is almost impossible to guarantee a high-quality app release even by using traditional rule-based automation testing.

So what’s the solution? It is high time to start investing in AI testing and machine learning algorithms so that you can improve the efficiency of your quality assurance testing scenarios. Not accustomed to these advanced workflows? Don’t worry! We are here for you. Our article will help you understand everything that you need to know to use machine learning to improve the efficiency, scalability, and quality of your QA release cycles. 

What Is AI Testing? 

Since what AI testing is is the primary question for this article, it is only fair that we begin our discussion with this question. It is the process of using artificial intelligence technologies like machine learning, natural language processing, and computer vision at various stages of the software development. 

The major aim of this inclusion is to improve the test creation process, test execution cycles, and bug detection. This makes sure that you can have an efficient testing and result analysis cycle. 

Compared to your traditional automation testing practices, the AI system will continuously adapt itself depending on the changing requirements of the application so that it can consistently ensure the accuracy of the test cases irrespective of the updates or change cycles. 

What Is A QA Scenario? 

Since we are trying to create a dynamic AI QA scenario using artificial intelligence and machine learning, let us divert our attention towards some of the major components of this workflow: 

• A dynamic QA will ensure that the software testing environment is consistently changing, so this means it will need the integration of continuous deployment and continuous integration cycles. 
• In this case, the user flows will change depending on real-time input and behavior that your application will face when it is released to the customers. 
• All the components of your testing, like APIs, data layers, and UI elements, are constantly updated to ensure that you are in line depending on all the changing requirements of the application release cycle. 
• Finally, in a dynamic QA scenario, the testing needs will also adapt to the continuous deployment and minimal manual intervention so that you can further optimize the resource and human requirements for the application release cycle. 

If you compare it with the traditional QA testing scenario, you will easily understand that it struggles with static test scripts, unstable UI tests, high maintenance costs, and also the inability to scale rapidly depending on the changing requirements of the application. 

How Machine Learning Implements Dynamic QA? 

Let us now divert our attention towards how you can implement dynamic QA testing in your software read cycles using the abilities of machine learning: 

Predictive Test Selection 

You can use the power of machine learning algorithms to understand the historical test execution data and identify the following parameters: 

• Which test case is most likely to fail during the execution process? 
• Which module that you have implemented in the application can generate a defect when it is being run? 

• How the changes made to the code base of the application can affect the overall behavior of the architecture? 

All this data will help you implement risk-based testing and test prioritization so that you can ensure that you’re using all your time and resources to ensure that at least the basic functioning of the application is available at all instances. 

A great example of this implementation would be to imagine a scenario where a logistic regression model can predict with 87% accuracy whether a regression test should be redone, depending on the recent code changes and all the historical records of previous pass and failure states. 

Anomaly Detection 

You can train your machine learning models on the expected performance metrics and also the behavior of your testing system. Therefore, it can automatically flag the changes that may not be explicitly covered on this training data and help you find errors within the core infrastructure of the application even before it becomes a major concern. 

To perform this process, the machine learning models usually use techniques like unsupervised learning. In this process, you have to use clustering and autoencoders so that you can uncover the hidden issues in large-scale log data and also the previous test results. 

Self-Healing Test Scripts 

The machine learning models will also help you to easily find changes in user interface identifiers like XPath and CSS locators. Depending on the findings, it can auto-correct them during the runtime so they can easily locate all the elements that are present on the application. This is a very important part to ensure that the apps remain functional even with multiple UI changes and updates. 

You can also use computer vision and natural language processing. To match visual elements or textual cues that can help the tools to remap the test steps intelligently. This approach will make it very easy to understand which element has caused the error during the test execution process. 

Test Data Generation 

You can use machine learning models to automatically create high-quality command lines and real test cases. These will be especially useful if you’re trying to conduct boundary testing, edge case simulation, and personalization scenarios.  

Machine learning models will help you in this process by going through the usage patterns and creating test data that can mimic real-world user behavior. This will be a very important step to improve the test relevance and scenario coverage, depending on the changing requirements and patterns of your user information. 

Automated Test Case Desig

If you are using traditional rule-based testing, you must have realized that it requires some form of technical knowledge of programming languages to create the automation test scripts.  

But gone are these days if you start using artificial intelligence and machine learning within your testing scenario. Why? Machine learning models will help you use natural language processing to change your plain English business requirements, user stories, and bug reports into highly targeted test cases. 

With this approach, you can massively reduce the requirements to implement manual effort to learn and create the automation test scripts. 

 
Defect Prediction And Classification 

Machine learning classifiers like Random Forests or Neural Networks can help you perform defect detection and classification. To further understand the implementation of this process, let us divert our attention towards some of the most effective use cases

• You can use this approach to predict potential hotspots for defects and also the areas within the application infrastructure that are most likely to develop a bug or error after the deployment process. 

• You can also categorize the issues that have already been detected within the application, depending on the severity and also the likely root cause for them. This categorization will also help you to streamline the troubleshooting steps that will follow soon after. 

• Finally, it will also help you in implementing Intelligent triage by tagging the developers with the right expertise. So this means that as soon as a bug is found in the application, the relevant team member will already be notified to take the required steps so the application can be moved further for the deployment stage. 

All these steps will be very important to optimize the feedback loop between the quality assurance teams and the development teams. It will also help ensure that everyone within the organization has a basic understanding of the current status of the development project. 

Ideal Practices For AI QA Testing 

Finally, let us go through some of the best practices that you must use for AI QA testing to improve the quality, scalability, and efficiency of your overall testing environment: 

• Even if you are using AI QA testing, it is very important to implement real device testing to guarantee the stability of your app. However, if you want to avoid the expense and hassle of a physical device lab, you should start investing in GenAI native test agents like KaneAI.

KaneAI by LambdaTest is a next-generation GenAI-native test agent – the industry’s first AI assistant that allows QA teams and developers to plan, author, execute, debug, and evolve end-to-end tests using natural language instead of writing code from scratch. With KaneAI, you can also generate app tests on the real device cloud.

• Since the concept of AI in software testing is relatively new, we would not recommend that you entirely migrate your testing environment to this process. So what’s the best process? You would choose a non-critical test within the environment and implement a prototype approach on it. Depending on the success and other metrics that we achieve here, you can scale accordingly.
  
  Even though AI QA testing is a very advanced process, you must always remember that it can never completely replace the requirement of a human developer. The best approach here would be to use AI for all the repetitive test cases and use human expertise for all the creative and complex testing scenarios. 

• While you are using AI testing, you should always remember that it is a constantly changing process. This means that various new technologies and additions will be constantly made in this segment, which requires your constant attention. However, before making any change, you should ensure that it will be particularly useful depending on your specific requirements. 

Apart from all the practices that we have mentioned above, it is also equally important to understand that there will be certain needs and strategies that will be specific to your requirements and choice. Therefore, we would recommend you to have a clear understanding of technological needs and the end goal for the application so that you can make these changes and strategies accordingly. 

The Bottom Line 

Based on all the factors that we went through in this article, we can easily come to the conclusion that AI testing using machine learning will help you change your expectations and the quality of the application that you are working on.

The intelligence and adaptability of artificial intelligence and machine learning will help you create better, faster, and more reliable software that will ultimately play a very important role in providing the best end-user experience to your customers, which will help strengthen your brand image. 

Want one final advice? Always look out for all the upcoming trends and innovations in this segment. Why? This will help you better adapt your testing approaches and also continuously improve the experience of your customers while using your app.