Are your testing processes consuming more and more resources? There is a high possibility that you’re still stuck with traditional manual testing or restricted automation testing processes. This is the perfect opportunity for you to start considering the integration of test AI within your testing environment. This will help you reshape the test landscape by boosting the efficiency of test creation, maintenance, and execution.
It is simply impossible to imagine the impact of test AI in the modern testing processes. Beginning all the way from AI-driven test automation to self-healing scripts, from predictive analytics to detecting bugs before they occur, there is simply no end to this.
Unsure about integrating test AI within your workflow? Don’t worry! We are here for you. Our article will help you understand what AI-powered testing consists of, how can you implement it, and why it stands out as one of the most important parts of the modern software development cycle.
The Lifecycle of Software Testing
Excited about implementing test AI within your system? Hold on! Let us take a step back and understand the entire journey that software testing has covered since the very start:
- Right at the start, testers had no other option but to create manual test cases and run them step by step to verify the app’s functioning. This process was not only massively resource intensive but was also prone to various human errors that resulted in doubting the final test reports.
- With a little evolution, the softwares became even more complex, and the testers had to use record-and-playback tools to automate the repetitive testing process. However, even with these inclusions, the tests were highly fragile, and even a minor change could completely break the entire workflow.
- With the arrival of various advanced tools like Selenium, Appium, and JUnit, the testers were able to automate various test cases using code-based scripts. However, even with this approach, the testers have to invest a significant amount of time to create and maintain the test scripts, and the system could not update these scripts depending on the UI changes.
- Finally, we are in a time when artificial intelligence and machine learning promise to fulfill all the shortcomings of the previous approaches. Using these inclusions, you can handle the test case creation process, diagnose failures, and even automatically adapt the tests to minor UI changes. Moreover, with the continuous development and innovation in this segment, you can only expect these to further improve and expand.
Why AI in Software Testing
Still confused about the influence of artificial intelligence in modern software testing? Let us divert our attention to some of the major areas of the testing cycle that can benefit with the inclusion of AI and ML:
- You can create, optimize, and maintain your test cases automatically without the slightest involvement of a human tester or developer.
- This inclusion will also help you to predict high-risk areas of the application or code that are most likely to fail. Then, you can use this information to guide all your testing efforts accordingly.
- It will be a massive boom to find the anomalies in large sets of data or test logs. Therefore you can use this inclusion to even uncover the most subtle bugs that are usually overlooked by human testers.
- With AI guiding you throughout the implementation process, you can rest assured that there is at least one test dedicated to every element present in an application to guarantee a comprehensive coverage.
- Artificial intelligence and machine learning algorithms will also analyze user behavior so that they can understand the most frequent workflows. This analysis will ensure that the application is tested based on parameters that matter the most.
Key AI-Driven Testing Capabilities
Now that we have developed a clear understanding about why should you bother integrating test AI within your workflow, we can now consider some of the advanced use cases that you can implement within your workflow by using artificial intelligence and machine learning:
1. Intelligent Test Generation
You can deploy multiple AI systems to analyze the application’s behavior, the user journeys, or past test data. Then, the system will recommend test scenarios by using the usage analytics, logs, or the application code structure.
For example, if you are working on an application that has functionality or source code which frequently changes and leads to multiple production issues, you can use artificial intelligence-driven test generation to create and prioritize these segments of the application. The final goal would be to ensure the basic function is available at all instances.
2. AI-Native Real Device Testing
There are various AI tools for developers, like LambdaTest which can allow you to implement real device testing using remote servers. To further improve our knowledge about this process, 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 10,000+ real devices, 3000+ browsers, and OS combinations. LambdaTest can also easily integrate with tools like Selenium and Appium to run various advanced processes like automated cross-browser testing and automated mobile app testing.
Want to learn more about this inclusion? The following sample code will help you understand how you can run automated cross-browser testing with LambdaTest:
3. Self-Healing Test Automation
Want to learn how self-healing test automation works with artificial intelligence and machine learning?
Traditional automated scripts often fail as they rely on fixed identifiers like XPath or CSS selectors. However, artificial intelligence gives the tool capabilities to track a broader context for each element. This can include textual context, relative position to other elements, or usage patterns. So if the primary locator fails, AI can always make an educated guess to still locate the correct element.
4. Natural Language Processing for Test Case Creation
This testing solution will help you analyze the user stories, requirement documents, or bug reports written in plain language. Depending on this analysis, the tool will generate test cases or Gherkin features automatically.
So, you no longer have to depend on the QA engineers to manually translate every requirement into test scripts. The result? You can massively cut down the test creation time and also ensure proper alignment with stakeholders who may not have adequate technical knowledge.
5. Predictive Analytics for Bug Detection
There are certain AI tools for developers which can go through historical data like defect logs and code commits. From the data that it gathers, it can build predictive models which can forecast the possibility of defects in a particular module or component of your application.
So, you can use this approach to focus all your testing efforts on the areas of the application which will make the greatest impact. The ultimate goal of this process is to reduce the risk of huge bugs or complete crashes in the app.
Best Practices For Using Test AI
Let us go through some of the best practices that we recommend the testers to incorporate within their AI testing environment:
- Artificial intelligence comes with a steep investment as you will have to get the licenses and other dependencies for the proper inclusion. Therefore, you must have different revenue plans that can help you justify this transition.
- Since AI is a completely new technology, you must create proper training sessions and workshops so that your testers and developers can get accustomed to this new technology.
- It is very important to always have some form of human supervision so that you can create a balance between human experience and artificial intelligence’s productivity.
- Since the training data is the most important component for any AI model, you must use high-quality data and monitor it continuously to ensure that it remains usable throughout the application development cycle.
- Finally, you must document all your findings regarding the test AI inclusion so that there is a reference point for any form of known issues and how to resolve them.
The Future of AI in Testing.
Based on our previous discussions, it might seem that artificial intelligence has already reached its peak in the segment of automation testing, right? You’re completely mistaken! Let us divert our attention to some of the upcoming revolutionary additions in the segment of test AI:
- Deep learning models will be soon able to mimic extremely realistic user flows. Therefore, you can not only use them to add straightforward inputs but also random variations in user behavior, environment changes, and network conditions.
- Beyond simple self-healing and test creation, artificial intelligence will be able to control the entire test planning strategies by suggesting the optimal mix of manual and automation tests. It can also identify performance test windows to recommend shift left actions for specific code modules.
- By now, you must have realized that testing is not an isolated process but needs the integration of continuous delivery and continuous deployment. The AI systems will be able to integrate within these pipelines with the help of real-time operational data to ensure consistent quality even in hyper-agile release cycles.
- You will be able to assess not just the historical defects but also real-time changes to refine the risk-based analysis. It will be able to implement all these inclusions in system architecture, code complexity, and user traffic segmentations. So, you’ll be able to use this process to ensure that the most important tests are executed early to ensure a consistent performance.
- AI will be able to provide advanced metrics that not only captures the functional greatness but also user satisfaction, trust, and brand reputation. Through all these sentimental data, you can create an application that transforms into a strategic positioning tool to implement a positive brand reputation.
The Final Thoughts
Based on all the factors that we discussed in this article, we can come to the conclusion that the arrival of AI in software development brings a massive jump in how organizations implement quality assurance. By adding all the contributions and best practices that we discussed in this article, AI will completely change the fundamental aspects of your QA testing scenarios.
The use of test AI will also have a very important role in creating the proper balance between rapid delivery and comprehensive application testing. This will ensure that you’re continuously providing the best end-user experience to your customers to rapidly increase your target audience.
Finally, you should also continuously grasp every opportunity to upskill yourself in this segment so that you can grasp all the new trends and innovations.
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