Still struggling to deploy optimal quality software? If we had to guess, we would say that you are still using manual and traditional automation testing to perform this process. So what’s the alternative? It is high time to start investing in AI QA testing.
The proper use of this process will ensure that your quality assurance testing processes are more predictive, efficient, and effective. This is one of the most important parameters of ensuring the quality, reliability, and performance of not only the software but also the entire testing environment.
Unaware about AI QA testing? Don’t worry! We are here for you. Our article will help you understand why AI-driven QA testing is the future of the software development and testing practices.
How Is Traditional QA Testing Limited
Before we answer “Why AI in QA testing?”, it is very important that you develop a clear idea about why traditional QA testing is no longer recommended for all the complex modern applications:
- If you’re still using human testers and script-based automation, you will quickly realize that you are failing to scale the overall testing infrastructure. This is because it can no longer keep up with the changing requirements of your rapidly evolving software projects.
- If you’re only using manual testing for QA test cases, you will have a huge risk of oversight and inconsistency that can come due to human fatigue and simple miscalculations.
- For traditional rule-based automation, you have to manually create the automation test scripts and also implement them. It will be a very slow process and will delay the product launches and also its subsequent updates.
- Whenever you will be adding any new testing method or integration, you will realize that your entire testing infrastructure is struggling to adapt with this new change. You can also face various inconsistencies and compatibility issues in this step.
What Is AI QA Testing
Now that we know all the challenges that you will face with traditional QA testing, it is time to divert our attention towards AI QA testing and how can it help you overcome all these challenges:
In this process, you will use machine learning algorithms, natural language processing capabilities, and predictive analytics with intelligent automation to overcome all the traditional quality assurance testing limitations. To shed more light over this segment, let us divert our attention to some of the major features of AI QA testing that can help you in this step:
1. Predictive Quality Assurance
With the power of artificial intelligence, the QA test cases can use predictive analytics to identify potential defects and risks depending on the historical data stored in the testing database.
This process will also help you predict where bugs will arise within the codebase before they can even become a concern. So, the testers can use this information to address issues before the application deployment to reduce the risk of product failures after it is released to your customers.
2. Intelligent Test Automation
With the help of artificial intelligence-powered tools, you can automatically create, execute, and manage the test cases depending on intelligent insights. This approach will massively reduce the manual efforts that were previously involved in all the testing processes.
You can also use this adaptive automation approach to implement continuous testing so that you can easily integrate agile and DevOps practices to improve the software delivery lifecycle while maintaining its accuracy and quality.
3. Enhanced Accuracy and Coverage
The algorithms of an AI-based testing environment will help you to automatically and dynamically create the test scenarios. These scenarios will also cover edge cases and complex user interactions, which manual testing processes often miss.
By using this comprehensive test coverage, you can release higher-quality software and also ensure minimal disruptions or errors after deployment. This is because you will be assured that there is at least one test case dedicated to every element of the application.
4. Real-time Feedback and Optimization
Artificial intelligence will help you to continuously monitor and analyze the test results so that you can have real-time feedback about its functioning. You can also use this approach to perform immediate adjustments and continuous optimizations of your testing strategies. This is a very important parameter to implement rapid identification and resolution of all the critical bugs and errors.
Key Advantages of AI QA
So now that we know all the features of AI QA testing, which can help you overcome the challenges of traditional testing practices, how do these actually benefit the process? To shed more light over this segment, we have mentioned all the advantages of implementing AI QA testing:
- By implementing AI-driven QA testing, you can improve the efficiency of your testing processes as you will be able to automate the repetitive and time-consuming tasks. You can also free up the human tester’s time so that they can focus on the strategic and creative aspects of the testing process. This will be a perfect balance of productivity and accuracy.
- With the help of AI testing tools, you can reduce the need for extensive manual testing and also the reliance on costly script maintenance. This will be a very important part of massively decreasing the overall testing expenses and improving the revenue of your test cycles.
- Since AI QA testing promises faster test execution and issue resolution, the products will reach the markets very quickly. Therefore, AI IQ testing will have a critical role in ensuring that high-quality software is delivered quickly to provide a competitive edge to your business as a whole.
- If you are using machine learning algorithms for quality assurance, they will continuously learn and adapt from the test data and user interactions. This approach ensures improvement in testing accuracy and also effectiveness over time. So, it will be a very important parameter of ensuring the highest quality of application over a longer period of time.
Best Practices For AI QA Testing
Finally, let us divert our attention towards some of the major practices that we recommend the testers to follow while using AI QA testing. These practices will not only help to improve the quality of your application but also boost the productivity of the entire infrastructure:
Start Small
Since the entire concept of artificial intelligence in software testing is completely new to the industry, it is very important to have some form of preventive measures in case something goes wrong.
One of the best ways of implementing this approach is to start small with a non-critical test within the test environment. After this, you should closely monitor the functioning of the test cases and its output. Depending on the results that you receive in this process, you can scale accordingly.
This approach will also be highly effective to gain the initial trust of the stakeholders who might be skeptical of using an experimental approach for software testing and quality assurance.
Invest In Learning
Although artificial intelligence is quite easy to adopt and straightforward, you need some form of learning curve so that all your existing testers and developers can get accustomed to this new approach.
So it will be a very good step to invest in workshops, seminars, and hands-on sessions to get your team started with this new approach. If you invest in proper training sessions, you can massively reduce the learning curve and ensure the sustained productivity of a test environment.
Use Cloud Testing
While you are transitioning to AI testing, you should also remember the importance of real device testing to guarantee the proper functioning of your application when exposed to various physical parameters of your device.
If you want to find a middle ground that helps you achieve both these processes, you should start investing in AI cloud testing platforms like LambdaTest.
LambdaTest is an AI-native test orchestration and execution platform that lets you perform manual and automation testing at scale with over 3000+ browsers, OS combinations, and 5000+ real devices.
To assist in your AI QA test cases, LambdaTest will automatically create a very detailed test report with multiple media elements like screenshots and videos. The goal of this process is to help you find all the faulty elements in your application and take the required actions.
Use High-Quality Data
You must always remember that the training data is the backbone for any artificial intelligence or machine learning workflow. So it is very important to invest in high-quality data like historical test logs, user information, and previously known errors.
With the help of the above process, you will be able to assist the AI algorithm to develop a baseline about all the known errors and the expectations for the application that is currently being tested. This is a very important parameter to boost the accuracy and also effectiveness of the testing infrastructure.
You should constantly update the test data for the AI QA test cases and also retrain the model so that you’re incorporating all the changes and new additions to the application infrastructure.
Use Human Oversight
Although AI QA testing is a very advanced process, you must remember that it can never completely eliminate the requirement for a human tester. The perfect balance is to maintain some form of human supervision to ensure that the model functions as expected and does not cross the set boundaries.
This is because if there is some form of AI malfunction and you end up releasing a faulty application, it can have a massively negative impact on your entire brand image.
Apart from the above best practices, you should also remember that you need to add certain unique practices that will be customized depending on the specific requirements of your application project. To understand which practices will suit you the best, you must develop a thorough understanding about your project requirements and personal needs.
The Bottom Line
Based on all the areas that we explored in this article, we can easily come to the conclusion that AI-driven quality assurance is a very big change in the modern software testing. If you properly use the capabilities of AI QA testing, you can ensure the quality and reliability of your entire test environment.
Moreover, by implementing all the best practices that we gave in this article, you can also ensure that your test cases are intelligent, predictive, and adaptive to all the changes and improvements that you’ll be adding to the application in the coming years.
You should also remember that a human oversight will still be essential in this mix, so that you can guarantee a high-quality application which will build a positive brand reputation and also expand your audience reach.
