AI QA: 5 Steps to Automate Bug Detection and Regression

Are you still using manual processes to verify the reliability of your application? We must say that it is a very dangerous process, as you might be gambling away the entire reputation of your brand. The main reason for this is the rising scale, complexity, and speed of modern applications, traditional testing processes are the only bottleneck that you will have. So, what is the way out? It's simple! You need to start investing in AI QA testing.

Not familiar with AI QA testing? You do not have to worry! We are here for you. Our article will help you understand the five most important steps to automate the bug detection process and regression testing cycles with artificial intelligence and machine learning algorithms.

What Is AI QA Testing

Since we're trying to spread awareness regarding the importance of AI QA testing, it is only relevant that we start our discussion with the basics of this process. So, it is the approach of using artificial intelligence and machine learning to run the quality assurance testing processes.

With this step, you can feed all the previously known errors, bug analysis reports, historical testing data, and the business requirements to the AI model. Depending on all the knowledge that it will gain from this step, it can create the test scripts that will ensure the proper reliability, quality, and scalability of the application.

The major benefit of AI QA testing is that the testing infrastructure will continuously learn and adapt itself depending on the changing requirements of the application and also your evolving business requirements. It will ensure that the testing environment remains relevant in the longer run to provide the best outcomes to your company.

It also eliminates all the manual effort that you have been previously putting into verifying the proper functioning and integrity of the application infrastructure after every minor update. You can use this time instead to focus on other creative and complex areas of the project.

Steps To Automate Bug Detection And Regression

Let us now divert our attention towards some of the most important steps that you need to follow to automate the bug detection and regression processes. While creating these steps, we paid proper attention to cover all the business requirements of every possible use case:

1. Use AI-Powered Test Generation

If you are still using manual efforts to create the automation test cases, you must have realized that it is not only a time-consuming process but also exposed to human errors. This is because the human testers might often overlook the errors, especially while working on complex business applications.

To solve this issue, you can start investing in AI-powered QA tools like Testim, Mabel, and Functionize. These tools will help you implement machine learning models to understand the user behavior, past bugs, and application code. Depending on the knowledge that you can gain, you can automatically suggest or generate the test cases.

If you're curious to know how this process functions, the entire infrastructure will use natural language processing to convert human-written requirements into test scripts that can be understood and executed by the testing environment.

For example, you can feed functional specifications or user stories into an NLP-powered AI model. Depending on this knowledge, it can auto-generate the test cases on user paths and previous issue history. After the test execution process, the QA engineers can validate, modify, or enrich these test cases to further improve the efficiency of the environment.

So, the major benefits of this implementation include faster onboarding of new modules, increased test coverage across user flows, and identification of corner cases that are often removed in manual executions.

2. Integrate AI-Powered Visual Regression Tools

With traditional automation testing, you are mostly focusing on logic and functionality while performing visual testing. But when it comes to user interface changes like misplaced buttons, font issues, or layered shapes, it can completely change the user experience of your application.

To properly ensure the implementation of this process, you need to perform advanced processes like pixel-by-pixel comparison, which will find noise and all possibilities of false positives in the test reports.

With the integration of AI QA testing, you can use tools like AppliTools or visual AI along with Selenium to use the power of artificial intelligence for understanding UI elements instead of just comparing pixel screenshots. This means you will be able to find true visual bugs while ignoring dynamic content or minor non-functional differences.

To implement this process, you first have to find a baseline screenshot of every user interface element. Then, after the deployment of new code, the artificial intelligence model will compare the new UI to the baseline image. Finally, the machine learning model will filter out expected dynamic changes and highlight the actual changes in the user interface.

So, the major advantages of this implementation include reduced false positives, high scalability across responsive designs, and also improved confidence in UI consistency across multiple browsers and devices. 

3. Predict And Prevent Bugs Using Historical Data

Reactive QA is a process where you find bugs after they occur. On the other hand, with AI QA testing, you can implement predictive QA, which will anticipate where bugs are most likely to occur depending on trends, usage patterns, and historical defects that are already present within the infrastructure of the application.

To perform this process, you have to feed the bug history, commit logs, change impact data, and user analytics into a machine learning model. After it gets all the data, it can score features or modules by the historical defect rate, highlight risky commits using models like decision trees or neural networks, and suggest code review or intensive testing zones.

To further utilize the implementation of this process, you can start investing in dedicated tools like Sea Lights and Launchable. You can also consider using custom modules built on tools like TensorFlow to implement internal analytics.

The proper implementation of this entire workflow will help you achieve preventive testing strategies, resource allocation based on risk scoring, and early warnings to developers before production leaks. 

4. Automate Bug Detection With Anomaly Detection Models

With traditional testing scripts, you can only find what they are designed for. But with the implementation of advanced processes like anomaly detection, you can discover unexpected behaviors during live runs or continuous integration and continuous deployment pipelines.

Anomaly detection is a part of an AI model which can continuously monitor the application logs, performance metrics, and user session data to detect flaws that are usually behaviors which are completely different from the normal functioning of the application.

To implement this process, you can use auto encoders for detecting anomalies in logs, time stamps, models for performance degradation, and clustering algorithms to group sessions and identify missing patterns within the application infrastructure.

You can also integrate the anomaly detection alerts into your DevOps methodologies to automatically trigger regression tests when anomalies are found. This will help ensure that all the bugs and errors are handled before they can turn into a serious concern to the major functioning of the application.

Some of the greatest benefits of this process include zero-touch identification of latent bugs, real-time alerting on unexpected application behavior, and improved mean time to resolution.

5. Perform Real Device Testing With AI Cloud Testing

With traditional practices, you need to set up an in-house device testing lab to understand the functioning of the application when it is exposed to the physical parameters of a device. However, as you all know that thousands of devices come to the market every year, which means that you have to invest millions of dollars to not only set up this lab but also ensure that it remains updated.

But as soon as you start investing in AI testing, you can use cloud testing platforms like LambdaTest to gain access to thousands of devices, operating systems, and browsers that are stored on multiple remote servers. To further understand the rule of AI-based cloud platforms, let us use the example of LambdaTest as our reference point:

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.

You can also easily integrate LambdaTest with automation frameworks like Selenium and Appium to run advanced testing processes like automated cross-browser testing and automated mobile app testing.

After running all your targeted test cases, LambdaTest will also provide you with the detailed test reports that will consist of multiple important testing parameters like screenshots and videos. The purpose of these inclusions are to help you quickly find the faulty elements and implement the required debugging steps.

Best Practices For AI QA Testing

While you are implementing AI testing for your quality assurance testing processes, it is very important to adopt the following best practices to ensure that you're utilizing the full potential of this advanced testing framework:

• As you'll be migrating to a very new technology, we would never recommend you to entirely run all your test cases with this technology. Instead, a wise choice would be to run a pilot project on a non-critical test case and migrate the results that you will achieve here. Depending on this, you can scale accordingly.

• Artificial intelligence in software testing is a massively changing industry. It is very important to constantly look out for all the upcoming trends and innovations in this segment. This will help you to quickly integrate them within your application infrastructure to further improve the user experience on your apps.

• Even if you start investing in AI QA testing, you should always remember that you can never completely eliminate the human in the loop. This is because the human expertise will be very important for complex and creative areas of the software testing process.

The Bottom Line

Based on everything that we discussed in this article, we can easily say that AI QA testing is completely changing how you approach the modern software testing processes. By implementing all the steps that we discussed in this article, you can not only predict the bugs in the core infrastructure of the application but also optimize the regression test cases to ensure that your app not only provides a dependable performance but also stands up to the expectations of your brand image.

As testers, you must start adopting artificial intelligence and machine learning to ensure higher product quality, faster release cycles, and a more strategic value to the engineering pipelines.