Tired of manually writing and constantly maintaining regression tests? Losing time on visual interface validation across dozens of devices?
In this article, I’ll show you how AI testing tools can help you optimize your work, taking over the most repetitive and tedious tasks.
AI tools vs. traditional testing methods
AI tools are solutions that use machine learning algorithms to optimize and automate testing tasks.
Unlike traditional scripts, which are static and require continuous maintenance, AI-based solutions can learn and adapt to changes in the app in real time.
Using AI in testing allows QA teams to implement more effective software testing strategies, while testers can focus on more complex tasks, such as exploratory testing or UX validation.
How AI helps mobile app testers?
In mobile app testing, AI enables the execution of tasks that would be extremely time-consuming or even impossible to perform using traditional testing methods. How exactly can artificial intelligence be used to assist with QA activities?
AI in test automation
Algorithms analyze and automatically adjust test scripts, reducing the need for manual maintenance. This is especially evident with so-called “self-healing” scripts, which repair themselves when user interface (UI) elements are changed.
The use of machine learning enables the creation of tests that are more flexible and resilient to changes in code.
Example
Imagine a button name in an app changes from “Zaloguj” to “Log in.” A traditional script would fail. AI tools can automatically recognize this change and adapt, saving you a lot of time on fixing scripts.
AI-Powered visual regression
One of the most practical applications of AI in mobile testing is visual validation. Machine learning algorithms can compare screenshots from different versions of an app and instantly detect visual errors, such as misaligned elements, changes in colors, or fonts.
This is invaluable for regression testing across many devices with various resolutions, where manual comparison would be impractical and time-consuming.
Intelligent test case generation
Artificial intelligence can create a test by analyzing historical data, logs, and even user behavior.
AI-powered tools can generate test cases that mimic realistic user paths, which helps to uncover bugs that might have been missed in manual testing.
AI can also provide personalized suggestions, pointing to app areas that require in-depth checks.
Real-time performance analysis
AI-driven tools can monitor and analyze an app’s performance in real time.
During testing, AI can simulate load and simultaneously monitor key metrics like CPU usage, memory consumption, and battery drain, which is crucial for software development.
This allows for the early detection of performance issues that could negatively impact the user experience.
Defect Prediction and Prioritization
Tools that use machine learning can analyze historical bug data, such as defect types, their locations in the code, and even related changes.
Based on this data analysis, AI can predict which areas of an app are most likely to have new bugs. This allows testers to prioritize their testing strategies and focus their efforts on the riskiest modules.
Example
AI may identify that after the latest update the loading time of the page view has increased to 15% only for users in a specific browser, which suggest performance issues.
User behavior analysis
Artificial intelligence can process vast amounts of data on user behavior. AI-powered tools can analyze how users interact with an app, which features are most frequently used, and which paths lead to app abandonment.
This information is then used to generate new, realistic test cases that reflect real-world usage scenarios. This allows for the discovery of bugs in areas that might be overlooked by traditional testing methods.
Example
AI analyzes user interactions with an e-commerce app on the path from the home view to the shopping cart and generates test scripts based on this.
Personalized suggestions and support in exploratory testing
During exploratory testing, where a tester freely explores an app to find bugs, AI can act as an assistant.
AI-powered tools can provide personalized suggestions for unusual paths worth checking or point out areas that have recently undergone modifications.
This use of AI in testing allows for a more precise identification of potentially problematic areas.
What AI tools for testers are worth trying?
The AI boom has led to a wide selection of AI-powered tools for QA testers. However, not all solutions are equally effective, especially since every software tester has different needs for a given project, and their testing strategies can also vary.
The tools described below are a subjective selection of solutions that, in my opinion, can genuinely help in various areas of mobile app testing.
Automation and test maintenance
- Mabl
This is a test automation platform known for its “self-healing” feature. Mabl’s AI learns the app and repairs test scripts when locators (unique identifiers for UI elements) change.
It is easy to use and scalable, making it perfect for teams that want to quickly create and maintain a large number of tests without getting bogged down in technical details.
Screenshot of the Mabl platform.
- Testim.io
This tool focuses on creating stable, AI-powered tests. Instead of relying solely on locators, Testim.io analyzes the relationships between UI elements and uses machine learning to build tests that are more resistant to code changes.
It is an ideal solution in environments where developers frequently modify the page structure, which, with traditional testing methods, often leads to “brittle” tests and instability.
Screenshots of the Testim platform.
- Functionize
Functionize stands out from other tools with its ability to create and explore tests. The AI in Functionize doesn’t wait for ready-made test cases; it independently explores the app and generates new scenarios.
This is especially useful in software testing, where the goal is to discover unexpected user paths and unique bugs. Functionize combines automation with elements of exploratory testing, which makes it a unique tool on the market.
URL: https://www.functionize.com/
Visual UI tests
- Applitools
This tool is useful for visual testing. It uses AI-based algorithms to verify the appearance of the user interface on different devices and at various resolutions. Instead of doing a pixel-by-pixel comparison, which is the standard traditional testing method, Applitools uses machine learning algorithms to recognize objects on the screen.
This allows it to ignore insignificant changes, such as differences in anti-aliasing or shadows, while still detecting even the smallest visual errors—like misaligned elements, or changes in colors or fonts.
This is especially valuable in software testing because it automates a process that is extremely time-consuming and prone to errors when performed manually.
Screenshot of the Applitools platform.
Testing on real devices
- Kobiton
It is a platform that provides a cloud of real mobile devices. It enables both manual and automated tests to be carried out.
Screenshots of the Kobiton platform.
What sets Kobiton apart is its ability to collect detailed real-time performance data, such as CPU and RAM usage, as well as the app’s impact on the battery. AI analyzes these metrics, allowing for the identification of infrastructure bottlenecks even before deployment.
Screenshots of the Kobiton platform.
URL: https://kobiton.com/
- Perfecto
Similarly to Kobiton, Perfecto offers testing on real devices in the cloud. However, what sets this tool apart is its focus on scalability and support for various technologies (e.g., IoT, web, mobile). AI in Perfecto is integrated with reporting functions, which allows for intelligent grouping of similar bugs and providing personalized recommendations.
It is ideal for large enterprises that need a stable and versatile platform for large-scale testing.
Screenshot of the Perfecto platform.
Test generation
- Test.io
This tool stands out from others because it can autonomously create test cases without a tester having to write the scripts. The AI in Test.io analyzes the app, identifies UI elements, and generates test scenarios based on the discovered objects.
This is particularly useful because it shortens the time needed to create regression tests and allows for the automatic creation of new tests in response to changes in the app.
Test.io is an ideal solution for teams that want to achieve a high level of automation in a short amount of time.
Screenshot of the Test.io platform.
URL: https://test.io/
API tests and integrations
- Qyrus
This tool focuses on API and integration testing. Unlike platforms that primarily concentrate on UI tests, Qyrus uses machine learning to automate the process of creating and validating API tests.
This is a key element in software development, as it allows for the detection of bugs in the backend layer even before they are revealed in the interface.
The tool can also integrate with popular continuous integration (CI/CD) tools like Jenkins and Azure DevOps, which streamlines testing strategies and allows for API tests to be run automatically after every code change.
No-code testing
- TestGrid
TestGrid combines AI with a no-code approach, which means testers can create UI test automation even without advanced programming knowledge.
Instead of writing scripts, users can simply record their interactions with the app, while the AI in the background creates and maintains the tests. The tool automatically generates test cases, and thanks to its “auto-healing” feature, it can adapt to changes in the interface.
Additionally, TestGrid provides access to a pool of real mobile devices, enabling app verification under real-world conditions.
This solution is especially valuable for teams that want to accelerate the QA process, increase the participation of manual testers in automation, and lower the costs associated with maintaining complex testing frameworks.
Screenshot of the TestGrid platform.
URL: https://testgrid.io/
Result analysis and reporting
- Sauce Labs
Although it is a test automation platform, its true strength is its advanced analysis of data from executed tests. Sauce Labs’ AI allows for intelligent grouping of similar bugs and the identification of their source.
The tool can analyze data such as logs, test run videos, and even performance data, which allows testers to quickly pinpoint the problem. This tool saves time on manual result analysis, speeds up the testing process, and helps teams focus on fixing the most critical issues.
Will AI replace QA testers?
Although some have this concern, it is unlikely. AI-based tools will not completely replace testers; rather, they will accelerate their work by automating repetitive and time-consuming tasks. This enables QA teams to create more effective and change-resistant testing strategies.
The integration of AI into the software development lifecycle will continue to deepen. In the future, we can expect even more advanced algorithms that will be able to not only detect bugs but also suggest specific solutions.
These tools will increasingly combine functions from various areas, becoming comprehensive platforms that support the entire software quality management process. The role of artificial intelligence in testing will continue to grow, so it is worthwhile to stay up to date with the changes and test out different solutions.
