Leveraging AI for Mobile App Testing: Pros and Cons

Leveraging AI for Mobile App Testing: Pros and Cons

Keywords – no code automation mobile app testing, no code test automation tools

The use of mobile apps has become an integral part of our lives. From social media and messages to banking and shopping, mobile apps make services available anywhere. With the increased use and complexity of mobile apps, the quality of the mobile app experience is essential to achieve through rigorous testing for developers. 

This is where Artificial Intelligence (AI) and Machine Learning (ML) have the potential to make mobile app testing more intelligent, faster and more effective. Let’s examine how AI and ML are revolutionizing mobile application testing and assess the advantages along with possible disadvantages of AI-based testing for mobile applications.

Advantages of Using AI for Mobile Application Testing

Time-Saving Test Cycles via Automated Tests 

The most significant benefit of using no code automation mobile app testing is automated repetitive testing operations. AI-enabled tools automates activities such as UI testing, API testing, regression tests etc. without human assistance. For example, machine learning algorithms can be trained with UI test data in large quantities to learn how to test the UI elements. This AI testing does not require script coding for UI tests.

Test cycles can be covered faster by using AI and ML instead of manual efforts. AI testing is an efficient approach to reduce the time to market for mobile applications. To this end, enterprises are steadily shifting towards AI-powered intelligent testing platforms that allow large-scale test automation.

Increased Test Coverage through Data Analytics

AI and ML make it possible for testers to acquire intelligence from huge quantities of testing data. Various advanced data analytics techniques can be used to study usage patterns, most common bugs and problematic areas of an app.

While testing mobile applications, it is almost impossible to cover all the use cases manually. The AI tools can capture data from various user environments. They can use these volumes of crowd-sourced data to detect important issues and increase test coverage. When AI algorithms are trained properly, they find edge cases that human testers would probably miss.

For instance, machine learning methods such as clustering and classification allow testers to profile actual app usage in the world and organize their testing activities accordingly. Dynamic feedback is given by AI-based analytics to constantly enhance the test coverage. This helps improve the app’s quality and user interface.

No code automation mobile app testing tools can collect data across distinct user environments and analyze these huge amounts of crowd-sourced data to identify vital problems and enhance test coverage. Machine learning techniques such as clustering and classification are used by testers to understand the actual usage of real-world apps, which allows them to prioritize their testing efforts accordingly. 

AI-based analytics offer continuous feedback that helps enhance test coverage. For instance, AI algorithms can process large amounts of usage information from different browsers, operating systems and devices. They can identify connections between usage settings and crash reports. 

These observations allow testers to identify high-risk environments and prioritize test cases for different platforms. These smart analytics significantly reduce tester time that would have otherwise been used on repetitive tasks. As such, no code test automation tools allow intelligent automation which leads to better test coverage in various user environments and enhanced app quality.

Smarter Cross-browser and Cross-device Testing 

Testing mobile apps across the fragmentation of multiple operating systems, browsers and devices is not an easy task. This problem can be partially addressed by emulators but they have some limitations. Using real user data, cross-browser and cross-device testing is smartly performed by AI testing tools.

AI algorithms are capable of generating analytics of millions and millions of usage data, crowd-sourced across browsers, operating systems and devices. It can also detect relationships between usage environments and errors/crashes reported. These insights enable the testers to identify high-risk environments and prioritizing test-cases based on this data for different platforms.

For example, AI techniques such as natural language processing can analyze unstructured crash logs and map them to relevant operating systems. Such smart analytics saves a lot of tester time that would otherwise be used for repetitive tasks. Hence, AI allows for intelligent automation of mundane cross-browser and cross-device testing. This aids in providing a uniform mobile app user experience in varying environments.

Disadvantages of Using AI for Mobile App Testing

Requirement for High Volume of Training Data

One of the main drawbacks of AI testing tools is their need for big test datasets to train machine learning models correctly. As the algorithms are exposed to more diverse, real-world test data, the performance of AI testing gets better. First of all, most organizations have limited test data available. Smart testing vendors address this by crowdsourcing usage data from the wider population.

Interpretability of Results

Although no code test automation tools produce faster and smarter insights, the results are not always easy to interpret. For instance, machine learning approaches are often regarded as a black box whereby it is not possible to explain why such actions were taken. Inability to guess how the AI testing process is conducted may hamper debugging and fixing of identified issues.

High Costs of Implementation

The costs of obtaining, installing, and maintaining AI-based testing platforms make it difficult for smaller organizations to adopt them. While AI automation does help in reducing manual efforts over time, these often prove to be too expensive for most mobile app development firms to afford. Lack of qualified AI and data science talent is another factor contributing to the challenges involved in implementation.

AI and machine learning innovations have revolutionized mobile app testing through the automation of mundane activities and the provision of smart analytics. AI testing offers several significant advantages such as improved speed, efficiency and test coverage but it also has some drawbacks. Organizations need to determine if the benefits of harnessing AI justify the investments and change management that are required. In the future, AI is likely to become essential for competing in terms of quality and speed with mobile app development and testing.