Have you encountered a scenario where you felt that even automation testing wasn’t sufficient to meet your requirements? Yes? Then it’s the perfect time to incorporate AI testing within your application development and testing infrastructure. Artificial intelligence in software testing helps achieve certain goals that are almost impossible with manual testing and even traditional automation testing practices.
Unfamiliar with AI testing? Don’t worry! We are here for you. Our article will help you understand how you can automate modern software testing processes by using artificial intelligence. During our discussion, we will also go through some of the best practices, tools, and challenges that you must be aware of in this step.
What Is AI Testing
Before we start understanding how to integrate AI in software testing, we must develop a clear idea about what is AI testing so that you can understand the areas which it can serve the best.
This is the process of using artificial intelligence technologies like machine learning, deep learning language processing, and predictive analytics to automate and enhance various parts of software testing. Compared to traditional automation frameworks, AI testing systems will learn from the data, improve over time, and adapt dynamically to the changing requirements of the testing environment.
The primary goal of using artificial intelligence in software testing is to reduce the manual involvement and massively increase the accuracy of the overall testing infrastructure.
Why AI Testing
Now that we have developed a clear understanding about what is AI testing, it is time to divert our attention to why should you even bother about integrating AI testing within your software development life cycle:
- AI-driven tools can identify test cases, scenarios, and pathways that might be overlooked by human testers or scripted test automation. To perform this process, it uses algorithms that can analyze huge data sets and historical testing data to predict and uncover critical high-risk areas.
- You can use AI systems to execute test cases faster and more accurately than human testers. This process will also help you eliminate common human errors and biases. So you can greatly reduce false positives and false negatives to create more reliable test results.
- If your team is using AI-driven testing, you can identify potential defects much earlier in the software development life cycle. The result? You can massively reduce the cost and effort that will be involved with removing these errors. This is because predictive analytics will help you flag the errors before they can even move on to the deployment phase.
- With traditional testing, you will require regular maintenance with the evolution of the software. However, AI-powered systems can automatically adjust to these changes so that they can reduce maintenance and allow continuous integration and continuous deployment pipelines to work smoothly throughout the workflow.
Applications of AI in Software Testing
Still unsure about using AI in software testing processes? Let us divert our attention towards some of the major use cases in the software testing life cycle that can benefit from the integration of artificial intelligence:
1. Test Case Generation
You can use artificial intelligence to automatically create comprehensive test cases. To implement this process, AI will analyze the requirements documentation, user stories, and historical defect data. Natural language processing techniques will also process the unstructured data to identify testable elements. This entire workflow will automate the creation of effective test cases.
2. Intelligent Test Execution
With artificial intelligence, you can ensure that your testing environment can dynamically execute the test to make intelligent decisions about prioritization depending on historical results, risk factors, and code changes. The implementation of such dynamic execution will massively improve the testing efficiency and will also have a significant stake in its accuracy.
3. Visual Testing and UI Validation
You can implement visual testing using artificial intelligence to identify discrepancies and anomalies in user interfaces. By using deep learning algorithms, artificial intelligence will compare the user interface screenshots against the baseline images. This implementation will rapidly highlight visual regressions that could impact the user experience after the application deployment.
4. Predictive Defect Analytics
Finally, artificial intelligence-infused predictive analytics tools can examine the historical defect data and source code changes.
Depending on the gained insights, it can pinpoint the modules that are most likely to fail during the execution process. You can use these predictive insights to prioritize the testing efforts and resources strategically to focus on these areas.
Implementing AI in Software Testing
Excited about implementing AI in software testing? Simply follow the steps that we have mentioned below in the correct order:
1. Data Collection And Preparation
You must begin this process by collecting high-quality data. A good approach will be to aggregate historical testing data, user feedback, defect logs, and code repositories. You must remember that clean unstructured data will ensure accurate AI modeling and predictions during the execution process.
2. Choosing The Right Tools
Want to use AI for software testing? Selecting the right AI-driven tools is the most important process of the execution phase. You can consider tools like Testim, Applitools or LambdaTest in this phase.
To further understand the role of the right tool in AI testing, 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 5000+ real devices, browsers, and OS combinations. You can also use this platform to extend your testing to outdated devices and legacy browser versions.
After running the test cases, LamdaTest will automatically provide you with a comprehensive test report, consisting of multiple screenshots and videos. It will be the perfect resource for finding all the faulty elements in the application and implementing the required debugging steps.
3. Training AI Models
The next step in this process is to train the AI models so that they can recognize patterns and behaviors relevant to their software. The initial training will be very important as poorly trained AI models can produce misleading reports. You should also focus on retraining the models and validating the data to maintain the accuracy of the testing process over time.
4. Integration with CI/CD Pipelines
The final step in this process is to integrate AI-driven testing with your existing continuous integration and continuous deployment workflows. This approach will ensure that the AI automation is triggered smoothly with every build. You can use it to significantly improve the development agility and also perform continuous delivery capabilities.
The major benefit of this process is that it helps you perform the development and testing phases at the same time so that you can quickly find the errors in the application infrastructure and sort them out before they become a crucial concern for the deployment phase.
Challenges in AI Testing
Although artificial intelligence is very efficient compared to traditional test automation, it also comes with its own share of challenges. To shed more light over this segment and help the new testers, we have mentioned some of the major challenges and their most effective solutions:
- Models will require large and accurate datasets to learn effectively. If you are using poor-quality data or insufficient quantity, it can massively degrade the effectiveness of your AI model. It is very important to properly focus on the test data and other parameters.
- The initial setup and investment in infrastructure for AI testing comes with a massive investment. Therefore you should have a proper return on investment and revenue plan so that you can easily justify this investment.
- To adopt AI testing in software development, you will need certain specialized skills in machine learning, data science, and test automation. In certain cases, the organizations face significant challenges in recruiting or upskilling their stuff accordingly. Therefore, it is very important to have proper learning programs or training setups to avoid this challenge.
- In most cases, your AI model will work as a black box which will make it difficult for testers to understand the reasoning behind all the decisions taken by it. If you trust the AI outcomes blindly, it can be massively challenging as it will reduce the transparency in the testing phase. The best way to eliminate this issue is to have a decisive role in the training phase so that you can know all the workflows that will be implemented by the AI model.
Ideal Practices for Successful AI Testing
Finally, let us go through some of the best practices that we recommend the testers to incorporate in the AI testing phase to ensure that you’re utilizing the proper efficiency and scalability of this advanced practice. While creating this list, we ensured that we’re covering all the common usage patterns:
- Since artificial intelligence will be a fairly new integration for almost all new testers, it is very important to begin with smaller pilot projects. Depending on the success of AI testing capabilities in these less critical areas, you can scale your testing environment accordingly.
- It is very important to create close collaboration between testers, developers, and data scientists. This approach will create a comprehensive understanding and effective implementation of AI testing strategies and capabilities.
- You must implement continuous monitoring practices to track the efficiency of your AI model. These practices will also help you grasp the effectiveness and drift over time so it is very important to ensure regular adjustments and retraining as required to ensure the overall efficiency of the testing infrastructure.
- We suggest the testers to combine AI testing with traditional testing methods to create the best balance within your infrastructure. It is also important to ensure that all AI test cases complement manual and automated testing to provide strategic enhancements rather than replacing the human judgment completely from the workflow.
The Bottom Line
Based on all the factors that we have put forward in this article, we can easily say that artificial intelligence is massively changing the software testing process by introducing intelligent autonomous and predictive capabilities. These capabilities far exceed the quality and expectations of our traditional testing strategies.
Although there are certain common challenges that are still associated with AI in software testing, you can use our best practices and strategies to easily mitigate these errors and ensure that you are providing the best end-user experience to your customers.
Moreover as AI will continue to evolve and adapt itself with the market, it will become a very important part of the software testing process that you just cannot ignore. So if you want innovation and quality assurance in your applications, AI testing is the way to go!
