AI Mobile App Testing - A Guide for Engineering Managers, Mobile Engineers, and QA Leads

Christian Schiller
March 16, 2024
min read


Mobile app testing faces challenges with traditional methods: manual testing is time-consuming, error-prone, and repetitive, while automated testing frameworks like Appium and Espresso require constant script maintenance and struggle with brittle tests and limited scope. AI-powered mobile app testing offers solutions by enhancing test specification, execution, and failure reporting. It streamlines test creation, adapts to UI changes, and provides detailed failure analysis. The integration of AI in testing reduces time, increases consistency, and supports cross-platform testing, offering significant benefits over traditional methods. Engineering teams considering AI adoption must evaluate time investment, team composition, and anticipated benefits, focusing on efficiency, reliability, and cost-effectiveness to improve mobile app quality and development processes.
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Common Challenges without AI Mobile Testing

The mobile app testing battlefield has traditionally been dominated by two main approaches: manual testing and automated testing frameworks. Let's delve into these methods and explore their limitations.

Manual Testing: The Tireless but Time-Consuming Warrior

Manual testing involves real people meticulously stepping through the app's functionalities, identifying bugs, and verifying expected behavior. While manual testing offers the advantage of human intuition for catching unexpected issues, it comes with significant drawbacks:

  • Time-consuming: Testing every scenario on a variety of devices can be a tedious and lengthy process, hindering development velocity.
  • Error-prone: Humans are susceptible to fatigue and oversight, leading to missed bugs.
  • Repetitive: Regression testing for bug fixes often involves rerunning the same manual tests, leading to tedium and potential inconsistencies.

Automated Testing Frameworks: Scripting the Path, But at a Cost

Automated testing frameworks like Appium, XCUI and Espresso offer a way to streamline repetitive tasks. These frameworks allow you to write scripts that automate user interactions, data entry, and assertion checks.

While code based test automation offers significant benefits, it has limitations for e2e testing:

  • Script Maintenance: As the app evolves, scripts need constant updates to reflect changes in UI elements and functionalities. This maintenance overhead can be significant.
  • Brittle Tests: Even minor UI changes can break automated tests, leading to "flaky" tests that pass intermittently and require debugging.
  • Limited Scope: Scripting every possible scenario can be challenging, and only early business engineers but not everyone can contribute.

These limitations of traditional testing methods highlight the need for a more intelligent and efficient approach. This is where AI-powered mobile app testing steps in, offering a significant leap forward in the quest for robust and efficient mobile app quality assurance.

Where is AI Useful in the Testing Process?

In the ever-evolving field of mobile testing, Artificial Intelligence (AI) is a game-changer, offering tools that significantly enhance the efficiency, robustness, and effectiveness of the testing process. This section delves into the multifaceted applications of AI in testing, highlighting how it transforms test specification, execution, and failure reporting.

Easy Test Specification

For entry-level non-engineers and busy mobile engineers alike, AI streamlines the test specification process. 

  • Leveraging AI, teams can create test prompts directly from video and audio recordings, significantly lowering the barrier to entry for test creation and ensuring tests are reflective of real-world user interactions. 
  • AI tools can create not only the ideal test scenarios but also a wide variety of other cases, increasing overall test coverage. 

This AI-driven approach not only democratizes test creation but also aligns testing efforts closely with user experiences and product specifications.

Robust Test Execution

AI enhances test execution across various platforms and devices through intelligent and adaptive testing strategies. 

  • By incorporating procedures to automatically handle unexpected screens, such as dismissing pop-ups or navigating through unforeseen prompts, AI ensures continuity and reliability in test execution. 
  • Additionally, when an element is not found, AI-driven tests can intelligently explore screens through scrolling or other navigational actions, mimicking user behavior to locate elements or pathways.
  • Self-learning loops from both failed and successful tests continuously refine test strategies, adapting to changes in the app and ensuring high coverage and effectiveness.

Helpful Failure Reporting

In the realm of failure analysis, AI significantly augments the depth and utility of reporting. 

  • By automatically checking logs (network, device) and configurations (feature flags), AI identifies the root causes of failures with high precision. 
  • Furthermore, it ranks failures by severity, prioritizing issues based on their impact on user experience or system stability. 

This targeted approach ensures that teams can quickly address critical issues, reducing downtime and improving the overall quality of the mobile application.

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How to Adopt AI in Testing Mobile Apps?

In the rapidly evolving landscape of mobile application development, the integration of Artificial Intelligence (AI) in testing practices is not just an advantage but a necessity. Engineering managers, QA leads, and mobile engineers often grapple with the intricacies of adopting AI for mobile app testing. The primary concerns include the time investment required, the team composition for initial Proof of Concept (PoC) projects, and the anticipated benefits. Understanding these aspects can significantly streamline the AI adoption process, making it a strategic move rather than a daunting task.

Time Investment for AI Adoption

The initial phase of integrating AI into mobile app testing requires a precise evaluation of the time investment. This phase includes setting up the environment, training the team, and developing the first set of AI-based test cases. Two of the most common scenarios illustrate the time dynamics well:

  • Automating Manual Regression Test Suite: Transitioning from manual to AI-powered automated regression testing can substantially enhance efficiency. For this transformation, a manual QA tester would typically spend about 15-30 minutes per test case. Additionally, a software engineer would need to dedicate around 2 days in total for test build provisioning and adjusting the CI/CD pipeline. The return on this time investment is observed within a few days to weeks, manifesting as a robust execution of tests across different platforms and languages, and the ability to handle unexpected pop-ups.
  • Reducing Flakiness in Appium/XCUI/Espresso Test Suites: Similar to the first scenario, the initial setup involves a QA engineer or software engineer spending about 15-30 minutes per test case. Plus, a software engineer’s 2-day support for test build provisioning and CI/CD pipeline adjustments. The outcome, like in the automation of manual tests, is a significant reduction in test flakiness, achieving a robust and automated regression suite within weeks.

Team Composition for PoC

For the initial PoC, a lean yet effective team composition is crucial. At a minimum, the team should include:

  • 1 Manual tester or QA Engineer: To identify and outline the test cases that are most suitable for automation and to provide insights into the current automation setup or manual testing process.
  • 1 Software Engineer: Responsible for setting up the necessary infrastructure, including test builds and integration with existing CI/CD pipelines, and for supporting the transition to automated testing.

Anticipated Benefits

Adopting AI in testing mobile apps yields substantial benefits, such as:

  • Efficiency: AI greatly reduces the time required to run test cases compared to manual testing and significantly lowers the maintenance effort compared to traditional scripting.
  • Consistency and Reliability: AI-driven tests offer consistent execution and can easily adapt to changes in the app’s UI or underlying logic, making them highly reliable.
  • Cross-Platform and Language Support: AI testing tools are generally designed to be platform-agnostic, allowing tests to run across iOS, Android, and web platforms, and to support multiple languages.
  • Handling of Dynamic Elements: AI excel at dealing with unexpected pop-ups and dynamic content, which are typically challenging for traditional automated tests.
  • Scalability: AI mobile testing allows for more test coverage without needing more effort in creating or maintaining tests.

Customer Success Stories

Concrete examples, such as Wealthsimple and Circle Medical, illustrate the practical benefits of AI in mobile app testing. WealthSimple automated 18 tests in just 2 weeks, and Circle Medical achieved similar success by reducing flakiness in their test suite with 10 tests automated in the same timeframe.

Adopting AI in mobile app testing is a transformative step that not only enhances testing efficiency and reliability but also aligns with the forward-looking practices in software development. By carefully considering the time investment, team composition, and expected benefits, engineering teams can effectively navigate the integration of AI into their testing workflows, leading to higher quality apps and a more streamlined development process.

How to Think About and Look at ROI in AI Mobile Testing?

In the evolving landscape of mobile application development, the integration of AI in testing processes marks a significant leap towards efficiency, reliability, and cost-effectiveness. Understanding the Return on Investment (ROI) in deploying AI for mobile testing is crucial for engineering organizations (Eng orgs) to make informed decisions. Currently, we observe three distinct cases in the industry:

Case 1: Companies with a dedicated QA Engineering Team 

For engineering organizations with Quality Assurance (QA) engineers already leveraging test automation, the focus is on enhancing existing frameworks with AI capabilities. The key ROI parameters for these organizations include:

  • Better Coverage: Aiming to increase test coverage from the current level to an ideal range of 80-90%, thereby ensuring a more comprehensive validation of the application.
  • Reporting Reliability/Flakiness of Test Runs: The primary challenge is the time-consuming nature of understanding why a test fails. AI can significantly reduce this by providing quicker, more insightful diagnostic information.
  • Cost Effectiveness: Implementing AI in test automation can lead to more efficient use of resources, reducing the overall cost of testing without compromising on quality. It can, for instance, save engineering time previously dedicated to maintaining element IDs essential for a robust automation suite.

Case 2: Companies with a dedicated manual QA Team 

Eng orgs that have not yet adopted test automation face a unique set of challenges and opportunities when considering AI for mobile testing. Their ROI parameters focus on:

  • Rapid Automation of Repetitive Regression Tests: Quickly automating the regression test suite to save time and reduce manual effort.
  • Handling Frequent UI Changes: Ensuring that tests remain robust and do not break with frequent UI updates, a common challenge in mobile application development.

Case 3: Companies without a dedicated QA Team

For organizations like Wealthsimple exploring end-to-end (E2E) automation options without a dedicated QA team presents a distinct set of ROI considerations:

  • Infrastructure and Maintenance Savings with GPT Driver: The attraction to solutions like GPT Driver lies in their ability to operate without significant investment in or maintenance of infrastructure. 
  • Lowering the Barrier for New Users: Furthermore, AI mobile testing allows technical and non-technical team members to dive into test writing without the prerequisite of extensive coding language and framework experience, significantly lowering the barrier to entry and maintenance efforts.

Each case highlights the diverse scenarios in which AI can transform mobile testing, driven by the specific needs and existing capabilities of engineering organizations. The common thread across these scenarios is the emphasis on enhancing test coverage, reliability, and cost-effectiveness, demonstrating the multifaceted value AI brings to the mobile testing landscape. By carefully considering these parameters, organizations can maximize their ROI and navigate the complexities of mobile application development with greater ease and efficiency.

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