Are you struggling with over-complicated test scenarios? Yes? This is because you are still stuck with traditional testing mechanisms while working with modern applications. Modern apps have adopted various new features and dynamic elements to maintain their reliability, stability, and user experience. So, you also have to integrate various advanced practices like AI in software testing to keep up with these changing requirements.
Unfamiliar with AI QA testing? Don’t worry! We’re here for you. Our article will help you understand how AI can help change your testing practices beginning from test design and going all the way to test execution and maintenance.
During our discussion, we’ll also go through various best practices and live examples to further help you understand the importance of artificial intelligence and machine learning in the modern testing scenario.
What is AI in Software Testing
Before we move on to all the complex discussions, it is very important to improve our basic understanding about artificial intelligence and why should we even bother about integrating it into the modern software testing process.
Artificial intelligence is currently one of the core elements of software testing strategies. It helps you in multiple areas, like predictive analytics, speech recognition, and smart test execution.
While implementing AI testing, you will use various algorithms and statistical models to streamline and optimize the testing activities. Your AI systems will learn from the historical data and predict potential failure points in the application. It will also help you to generate efficient test scenarios, intelligently prioritize test cases, and even self-heal the broken test scripts.
Using all these capabilities, the testers will be able to efficiently manage larger and more complex testing scenarios. This will have a very important role in improving the test efficiency and also decreasing the resource consumption of the overall testing workflow.
Major Use Cases Of AI-Driven Testing Solutions
To help you further understand how you can improve your testing solutions with AI, we have mentioned all the major use cases with its implementation. While creating this list, we made sure to cover all the prominent and most common additions to this list:
Test Case Generation and Prioritization
The most basic yet most effective use of AI in software testing is the test generation process:
- You can use AI to analyze the application’s usage pattern, identify test coverage, and propose new test scenarios that real users are most likely to encounter. So, these processes will not only help you cover all the elements of the application but also make sure that you are considering all the real-world scenarios.
- You can use machine learning models to suggest which test cases to run first or skip based on the past execution results. During this process, you can also consider code changes and risk metrics.
For example, a web-based e-commerce platform which has thousands of user interactions daily can use AI to analyze user interaction paths and recommend targeted test cases for popular checkout flows. So, the company can use this data to prioritize the testing scenarios instead of blindly running all the tests.
Self-Healing Test Scripts
If you’re working with a traditional automation test script, there’s a high possibility that it still relies on static locators like XPath or CSS selectors for element location. The immediate downside of this process is that if there is any UI change, the workflow of the testing process will completely break. Instead, you can use AI to:
- Implement adaptive locator strategies. Whenever an element locator fails, the artificial intelligence system will look for alternative attributes like text levels, relative positions, or visual patterns to re-identify the element.
- You can also implement continuous learning so that the self-healing frameworks can learn which locator strategies are more robust and then minimize the maintenance overhead.
For example, a retail application can change its “add to cart” button from an anchor tag to a button element. So, if you use a conventional script to test this button, it will completely fail. On the other hand, an AI-based system will change from button text or visual positioning to provide the same functioning while updating the locator.
AI-Powered Testing
With the inclusion of artificial intelligence, you can consider using AI-native testing platforms for implementing various modern testing parameters like real-device testing, load testing, and performance testing.
For example, 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. Using LambdaTest, you can implement AI based QA testing and also test AI elements present on your application’s user interface.
To implement performance testing, you can consider using dynamic workload modeling, proactive bottleneck detection, and adaptive testing. You can use all these features to analyze traffic patterns and simulate them to understand the breaking point of the application, identify potential performance bottlenecks based on resource allocation, and detect a slowdown in particular micro-service execution.
Automated Defect Detection and Triaging
Finally, after finding all the defects and errors using AI, you can report them with artificial intelligence to keep trap of bugs efficiently and also resolve them without complex errors:
- You can use artificial intelligence to implement log analysis so that you don’t have any unsupervised anomalies. These patterns will also help to spot unusual algorithms and connect them with the suspected element to implement the resolution steps.
- You can also use intelligence defect categorization with NLP-based classifiers to categorize the incoming bugs and assign them to the right teams. This approach is very important to ensure that the correct members of the team are in-charge of the bugs and also all the critical bugs and errors are being traced and resolved on priority.
- Finally, artificial intelligence can also help you to implement root cause analysis with the help of advanced machine learning models. This implementation will help you analyze trace logs and also system metrics so that you can find the root cause of all the systematic failures within the infrastructure of the application.
Apart from all the use cases that we have mentioned in this article, we also encourage the testers to consider exploring additional use cases that might be relevant depending on the particular requirements of your application.
Best Practices For Implementing AI in Testing
To ensure that you are using the full power of AI QA testing, we have mentioned some of the best practices that can help you avoid the common obstacles and also improve the quality of the overall testing workflow:
Start Small And Scale Gradually
One thing that the testers must remember is that you cannot change your entire QA process in a single day. So, the most effective approach is to begin with a targeted AI pilot project like using AI for different predictions or implementing self healing scripts for a single user interface flow.
If you achieve quick wins in the above workflow and have a measurable ROI, you can bring in more confidence and start implementing a similar approach in the other aspects of the application testing process.
Collaborate Across Teams
You will see that for implementing AI-based testing, you will have to add multiple stakeholders like developers, testers, press engineers and even DevOps engineers. This approach will help you to define the testing objectives, gather necessary data and also interpret the model outputs properly.
Focus On Data Management And Tooling
You must remember that your AI capability will only be as good as the data pipelines and tools which are responsible for feeding it. So, you must ensure that your set mechanism for collecting, storing, cleaning, and managing data is consistent and also abides by your testing results.
It is also important to properly understand AI-powered test automation tools on factors like ease of integration, scalability, and their ability to handle large, complex datasets. This will help you to understand if the chosen tool or platform is suited as per your testing requirements.
Monitor And Continuously Improve
Finally, you must remember that any AI platform or workflow will degrade over time. So, you must continuously monitor it and fine-tune the parameters as per your requirements and the needs of the application that is being developed.
You should also have periodic systems to add additional data or refine the existing algorithms within the AI architecture.
The Future Of AI QA Testing
Although it might seem that software testing has reached a very high limit, you’ll be amazed to see what additional trends are yet to come in this market. To shed more light over this segment, let us take a quick look at some of the most effective upcoming features in AI software testing:
- Auto-test generation from code will use advanced NLP and ML models so that you can generate test scenarios directly from the test code or user stories.
- You will start to see an increased usage of robotics process automation, which will combine AI testing to automate broader end-to-end business processes.
- The inclusion of AI in software testing will also continue to improve observation platforms like metrics, logs, and traces. So, AI will have access to more granular data to improve anomaly detection and predictive analytics.
- Although exploratory testing is mostly human-driven, the advancements in AI systems will propose real-time guidance or highlight suspicious areas for testers to explore.
- Finally, the use of ethical AI and implementation of regulatory compliances will ensure that you’re identifying all the possible business risks. You will also see the continued emphasis on building explainable AI models that can comply with all the data protection and fairness regulations.
The Bottom Line
Based on all the factors that we have put forward in this article, we can easily say that in this era of speed and innovation, software testing will be a very important part that can break or make your company’s reputation. By efficiently integrating AI with QA, you can ensure that you’re providing the best end-user experience to your customers, and that too within a short period.
AI QA will help the teams to plan, execute, and maintain the test suites while reducing the overall manual effort. These practices will also have a very important role in increasing the testing coverage and offering data-driven testing insights.
By integrating AI with the test reporting and maintenance process, you can also ensure that you are keeping track of all the current testing activities so that even the tiniest of bugs cannot flow into the test production phase. The integration of AI with advanced testing processes like RPA, cloud-native architectures, and DevOps practices will further improve the quality of modern QA practices.
