What if mobile app testing could go beyond ensuring features work correctly and instead investigate how different users would actually use them? This is becoming a reality as AI-assisted mobile app testing is increasingly adopted. In 2026, 58% of technology and engineering organizations have adopted AI Agents in testing and QA automation.
One innovation that can be considered in this regard is synthetic user testing, whereby AI-created personas simulate different approaches to navigating and completing tasks in an application. Let us find out how this concept differs from conventional testing, where it can add value, and how developers can integrate it into their mobile app development processes.
What are Synthetic Users in Mobile App Testing, and How Do They Differ from Traditional Application Testing?
The idea of synthetic users originally emerged in UX research. Nielsen Norman Group describes them as AI-generated representations of user groups that can produce simulated feedback without involving actual participants. The synthetic users consist of AI-generated personas of actual users who merely represent how other users would behave while using the mobile application. These synthetic users resemble real customers since every character possesses the features of a real user, for example, the device, proficiency with technology, patience, network connection, etc.
Unlike traditional testing, which follows a predetermined path, these independent Agentic Agents analyze every screen and take actions accordingly.
See the table to understand the difference between these two approaches:
| Dimension | Traditional automated testing | Synthetic-user testing |
|---|---|---|
| Test design | Predefined steps and assertions | Persona-informed goals and scenarios |
| User journeys | Mostly scripted | Can explore alternative paths |
| Adaptability | Scripts may need updating | AI agents can sometimes adapt to interface changes |
| Coverage | Specified test cases | Additional exploratory scenarios |
| Evaluation | Expected versus actual results | Goal completion and behavior-based observations |
Where Traditional Mobile App Testing Lacks and How Synthetic User Testing Fills the Gaps
Traditional mobile app testing proves to be useful in verifying if features function properly. However, we know that real users don’t always interact with an app the way developers expect. The points below show the real reason why old mobile app testing fails:
Unpredictable User Journeys
Since users don’t always follow the shortest path to complete a task. Synthetic user testing explores alternative routes to help mobile app developers spot confusing navigation, unnecessary steps, and unexpected dead ends.
Different Levels of User Familiarity
It is not easy for a novice user to perform what is evident to an expert user. Persona-based synthetic user testing assists the developers in analyzing how users with varying levels of technical expertise would use the application.
Frequent Interface Changes
Even small changes to an app’s interface can disrupt the whole automated test scripts. AI mobile app testing agents may adapt by recognizing updated controls without requiring every interaction to be manually rewritten, which, of course, saves time.
Usability Issues that Functional Tests Miss
An app can pass every functional test and still be frustrating to use. Synthetic users can reveal potentially confusing interactions that deserve further investigation before the app reaches customers.
How to Introduce Synthetic Users into Your Mobile App Development Process
If you want to add a synthetic user testing layer to mobile app testing, you first need to understand how people use your app, their struggles, and what tests should uncover. Let us see the steps below:
Step 1: Create Synthetic Personas Based on Real Users’ Behavior
The first thing that you need to do is to analyze what kind of information your existing user data can provide before you start creating synthetic personas. You can learn this information through product analytics, customer support tickets, usability reports, and other sources of knowledge about the issues that users have or drop tasks. Here comes a smarter way to approach the issue: pick a specific problem to test rather than making up a general persona.
Step 2: Choose Testing AI Agents Compatible with your Development Stack
The next step is figuring out how synthetic personas will interact with your application. This totally depends on the technology stack you’re working with. Espresso and UI Automator are tested technologies for automation of native Android apps, whereas XCUITest works well for automated testing of iOS. Also, you can use Appium for cross-platform app testing.
That said, compatibility should be taken into consideration, because not all AI testing agents are compatible with all frameworks, all platforms, or all execution environments. In case you are planning to invest in mobile app development services, this aspect should be discussed in advance to avoid rework during integration of AI-powered mobile app testing into your process.
Step 3: Incorporate Synthetic Tests into CI/CD Pipeline
Obviously, you won’t want your QA engineers to be setting up those synthetic-user tests every time a new build gets created. Thus, one way to start would be to pick some crucial user journeys like signing up, logging in, or checking out and then set up test accounts, test data, and predetermined states of the app.
So, now that everything is in place, it is the moment to tie the tests to your CI/CD pipeline. Whether to run tests on certain builds, code updates, or on certain testing schedules is totally up to you. Also, make sure that the system provides you with screenshots, action history, and logs from your app to investigate issues during testing only.
Step 4: Measure Whether Synthetic Testing is Actually Helping
It is pretty easy to get caught up in how many scenarios your autonomous app testing setup can execute. But running more tests doesn’t mean you are finding more problems. So, start by tracking how many genuine defects synthetic users identify before release. Then compare those findings with your existing automated tests to see if you are discovering anything new.
Another useful indicator is task completion. For example, if a synthetic user repeatedly fails to complete checkout, Mobile app developers need to investigate whether the app is causing the failure or the AI Agent is making incorrect decisions.
Step 5: Let the QA Teams Check AI-detected Issues
It’s crucial to remember that all errors detected by an AI testing agent aren’t defects of the application itself. The AI Agent may simply make a mistake, and taking all results as a bug may lead to investigating non-existent problems by your mobile app developers.
Hence, human intervention is necessary here. The QA engineers should analyze the work of the agent and check if there is a logical reason behind the reported error. In case of doubt, it’s possible to repeat the interaction and find out the reason behind it. Then you can add this defect to the regression testing process.
Step 6: Update Personas with Shifts in Behavior of Users
It is important not to forget about one detail: synthetic personas should be changeable during the lifetime of an application. This is due to the fact that users could begin performing their usual operations in a different way with new features and even interface alterations. Of course, testing scenarios should also adapt accordingly.
Try to make it a routine activity to compare synthetic persona data with actual user behavior. It is best when scenarios closely match real user cases.
The Future of AI-Assisted Mobile App Testing
Synthetic user testing helps overcome the inability to predict every possible user interaction with an existing application. In the future, this AI-assisted mobile app testing process may become even more automated, as synthetic personas can be created based on information about real interaction with the app. For instance, problems with payment verification can create a new scenario for tests prior to releasing the application.
However, greater Agentic AI automation doesn’t necessarily guarantee better testing. Therefore, mobile app developers and testers must continue to verify results, troubleshoot problems, and make release decisions. The true value of synthetic users is in their ability to combine wide test coverage with human knowledge to create more dependable mobile apps.
Author Bio:
Amelia Swank is a seasoned Digital Marketing Specialist at SunTec India with over eight years of experience in the IT industry. She excels in SEO, PPC, and content marketing, and is proficient in Google Analytics, SEMrush, and HubSpot. She is a subject matter expert in Application Development, Software Engineering, AI/ML, QA Testing, Cloud Management, DevOps, and Staff Augmentation (Hire mobile app developers, hire WordPress developers, and hire full-stack developers, etc.). Amelia stays updated with industry trends and loves experimenting with new marketing techniques.
