Are you struggling with the increasing complications in your application infrastructure? Then you must still be stuck with traditional rule-based automation testing practices. What’s the solution? It is the perfect opportunity to begin using AI QA testing workflow within your environment.
This inclusion will allow the quality assurance teams to access predictive analytics, automated testing, and intelligent decision-making to change how the software is tested and checked within agile environments.
Unfamiliar with AI QA testing? Don’t worry! We are here for you. Our article will help you understand all the major areas of quality assurance in an agile environment. We’ll also go through some of the best practices that can help you implement this approach within your infrastructure.
Role Of Agile QA Testing
To start understanding how artificial intelligence can change your quality assurance testing processes, it is very important to develop a clear idea about what the major expectations of an agile QA testing environment are. To shed more light on this segment, we have mentioned some of the major parameters below:
- You must begin the understanding process to have a clear idea about what your requirements are in terms of the testing infrastructure, and also for the application that you’re currently working on.
- As a QA tester, you will also be responsible for creating and maintaining all the automation test scripts that will verify the functioning of all the elements that you will be adding to the application.
- It is also one of your roles to implement manual testing parameters for verifying multiple edge cases and other exploratory use cases. In most scenarios, you will find that these test cases simply cannot be automated as there are various unpredictable parameters.
- You also have the role of maintaining the test in a continuous integration and continuous deployment pipeline. This approach will ensure that you receive quick feedback on the quality of your app’s source code.
- Finally, you also have to invest your time and resources to identify areas that are most likely to break during the test execution process. Depending on this understanding, you have to allocate your test cases accordingly.
Major Challenges In QA
Let us turn our attention towards a few of the huge challenges that you will face while implementing traditional QA test cases in an agile environment. The understanding of these parameters will help us find the basic goals that we will be aiming to solve with the integration of AI:
Increasing Complexity of Applications
Modern applications are aiming to integrate microservices, distributed architectures, and multiple third-party integrations to improve the experience for customers. However, the addition of all these parameters is creating an environment where even a small bug can destroy the entire workflow.
Therefore, you have to rely on comprehensive and frequent test execution processes to ensure the proper quality and stability of this infrastructure.
Limited Resources and Time Constraints
While working in an agile environment, you must have realized that you are forced to balance short deadlines and resource limitations. So, if you work on one of the applications that needs to be frequently rolled back to the development phase for removing every single error, it will not be feasible in these standards.
This is because, over time, the frequent execution of this process will lead to technical debt in testing frameworks, test scripts, and also a huge backlog of multiple unsolved defects.
Shifting Requirements
One thing that you must embed within your mind is that agile technologies will thrive with flexibility and also welcome all the changing requirements. But this integration will subsequently complicate the QA efforts.
You will realize that you are forced to continuously update the test cases and add new user stories to overcome unanticipated dependencies on the workflow. So, the only possible way to tackle all these challenges is to ensure that your entire procedure is organized and responsive.
Benefits of Integrating AI into Agile QA
Considering all the challenges of traditional QA testing processes, let us now divert our attention towards some of the major benefits of integrating AI within agile QA testing:
Increased Test Coverage and Speed
You’ll be pleased to know that with the use of artificial intelligence in QA testing, you can massively increase your overall test coverage. This is because the automated AI systems will run thousands of different tests in parallel.
It is also worth adding that all ML algorithms can learn from all your code changes to prioritize the test cases that are most relevant to the modified parts of the code. This entire workflow will ensure that you are able to find defects without allocating a lot of time and effort to the debugging stage.
Reduced Human Error
We have all discovered that the increased dependency on human testers can often cause various errors in the final Test reports that might degrade the quality of the application. So, by allocating all the repetitive testing tasks to the AI-driven QA system, you can reduce the errors that might arise due to human fatigue and oversight.
This is true when you are part of an agile test environment where all your tasks will be time-sensitive, and using AI testing tools will ensure consistency and high-quality testing.
Intelligent Test Automation and Maintenance
If you’re working with an AI-driven testing system, it can learn from user behaviors, code check-ins, and backlog items. Depending on this learning, it can create and update the test scripts as per your requirements.
For example, if a tester modifies a certain function, the AI system will automatically detect which test script needs to be updated or even generate a new test case, if required. So, using this self-healing approach, you can cut down the manual labor and also eliminate the brittleness of the test cases.
Smart Defect Detection and Prediction
Do you have to roll back the entire application to the production phase for every single defect? We have some good news for you! With AI testing, the system will go through all the records to find past defects and forecast the areas of the application that are most vulnerable to failure.
AI can put in more improvement by also understanding the code complexity, lines of code changed, or even the developer’s commit history. Depending on all this data, you can emphasize all your testing oversights in these high-risk areas to ensure that the risk of bugs arising in the production phase is hugely reduced.
Improved Collaboration and Communication
The core idea of agile is to improve the collaboration between all the teams of the organizations that will be involved in the application development and testing process.
Natural language processing and other AI-driven communication tools will help you go through large volumes of data, bug-tracking systems, project management platforms, and chat logs.
Depending on all the information that you gather from the above process, the tools can highlight patterns, critical issues, and even recommend solutions to overcome all these errors. Therefore, you can ensure that even the non-technical members of the organization can be involved in the testing process and have an idea about the current status.
Running AI-Driven QA in Agile Teams
Excited about using AI QA testing? Let us now divert our attention towards some of the major steps that you must follow to implement this process within your test environment:
Laying the Groundwork
Before you go all-in on AI QA testing, you have to focus on ensuring that all your QA testing processes and data collections are properly structured. This is because the higher quality data you use to train the AI model, the better its accuracy will be. This data can come from multiple sources, like bug reports, logs, historical test results, and coverage metrics.
1. Choosing the Right Tools
We strongly recommend that testers focus on choosing the right AI testing tools to ensure the proper productivity and scalability of the entire infrastructure. For example, if you want to introduce real device testing for the QA test cases, you can invest in cloud-based platforms like LambdaTest.
LambdaTest is an AI-native test execution platform that lets you perform manual and automation testing at scale with over 5000+ real devices, browsers, and OS combinations. This platform will also allow you to natively integrate various frameworks like Selenium and Appium to introduce advanced testing processes like automated cross-browser testing and automated mobile app testing.
Interested in learning more? The following code snippet will help you understand how you can run automated real-device testing with LambdaTest:
2. Training and Onboarding
With every new tool or framework, you have to consider the learning curve that comes along with it. Moreover, since the use of AI in application testing is a relatively new process, the learning curve should be significantly steeper.
So, it is very important to properly invest in multiple training sessions and workshops so that your testers and developers can get accustomed to this new technology. We also recommend the testers to choose certain tools that have relatively simple UI and easy to learn interface.
You should also focus on the training documentation, technical data, and community support that comes with certain AI testing tools.
3. Establishing Key Metrics
We suggest the testers to start implementing AI testing in a pilot project by only initiating it in a non-crucial testing process. This will help you understand how your infrastructure adapts to this new transition. Depending on the metrics that you achieve in this project, you can scale accordingly.
To understand the success of the AI QA implementation, you can measure metrics like defect detection rate, reduction in testing time, false positives and false negatives rates, automation coverage, and the overall mean time to repair.
The Bottom Line
Based on all the factors that we went through in this article, we can safely say that agile methodologies require quick and iterative changes when working with QA test cases. But without the adoption of AI QA, you will be struggling to meet all these requirements. This is because although the combination of manual testing and rule-based automation might be effective, you simply cannot scale with these technologies.
So by integrating artificial intelligence with QA, you will be able to not only scale the testing environment but also improve, reduce, and adapt the test scripts as per the changing requirements of the application.
We also recommend the testers to look out for all the upcoming trends and innovations in this segment, as the integration of all these parameters will ensure that you are providing the best end-user experience to your customers. Factors like these go a long way to build and maintain a positive brand reputation while expanding the audience base.
