The Latest Techniques & Best Practices

Artificial intelligence (AI) is revolutionizing the software testing landscape by offering an efficient, effective alternative to manual and traditional test automation approaches. While classical methods have their merits, they often struggle to keep pace with modern software applications' increasing complexity and rapid development cycles. AI-driven testing distinguishes itself by leveraging advanced algorithms, machine learning, and data analysis to tackle these challenges head-on.

By automating and optimizing various aspects of the testing process, AI enables engineers to work more efficiently, uncover defects more effectively, and ensure comprehensive test coverage. From intelligent test case generation and real-time anomaly detection to visual analysis and living documentation, AI-powered testing tools and techniques adapt to the ever-changing landscape of software development. This empowers teams to deliver higher-quality applications, reduce time to market, and enhance the overall user experience, raising the software quality assurance bar to new heights.

Key AI testing concepts 

Test case generation with AI

AI-driven testing improves test case generation by using user data to create relevant and comprehensive test cases. AI algorithms analyze real-life customer interactions, identify the most frequently used product paths, and prioritize test coverage accordingly. This data-driven approach ensures that testing efforts focus on the areas that matter most to end-users, resulting in a more efficient and effective testing process.

{{banner-large-dark="/banners"}}

AI-generated test cases cover every possible product path, including edge cases and scenarios that human testers might miss. This thorough coverage helps uncover hidden bugs and vulnerabilities, leading to more robust and reliable software. AI can also generate test cases much faster than manual methods, enabling teams to increase velocity and speed up the delivery of their projects and features.

Let’s look at an example from the AI-powered tool Qualiti to demonstrate this concept. Here’s  an overview of the test cases:

The system generates each test case from data collected in real time within the targeted application. In this case, the application under test has an embedded script that monitors and records user actions. This collected data can be passed to a model to identify patterns within user behavior. Over time, as data is continually collected, the model is trained on each new data set to understand user behavior better and make updates. The fully trained model can then generate and maintain test cases.

Taking a closer look at individual test cases, we can see the five individual steps produced by the AI tool by identifying a usage pattern during live transactions:

1. Navigate to a product admin page 
2. Click on the menu
3. Type an employee’s name
4. Click on the result that appears after typing
5. And finally, click on the search button
   
   

The AI-powered tool implements the test case in this example and will exercise the test steps each run. The best part is that the AI can adapt and refine test cases based on the latest data. This dynamic approach to test case generation ensures that testing remains current and aligned with the evolving needs of the user base. 

Easy test maintenance

Test case maintenance is a time-consuming task that requires engineers' significant effort, but AI can take over this responsibility, fixing tests as needed without requiring manual input. This lets the team focus on more critical tasks, improving overall team productivity. AI can automatically update test cases to address issues such as changes in the application under test, new features, or modifications to the test environment.

A tool like Qualiti showcases the potential of AI assistance. In this test case, let's examine each automated test step for the example of searching an employee's name within an application:

This test step clicks on an element in the application UI by searching for a specific URL reference (href) on the page. Of course, this link could always change, making the test fail. The test will be flagged if it fails, and an engineer can determine if the change is an actual defect; if not, the test code can be updated. Typically, an engineer would then need to find the new href and update the test steps, but AI tools can handle this manual process automatically. The AI will rerun the tests after updating the selector to ensure that tests account for new changes in the application.

As the application evolves and new requirements emerge, AI tools can quickly adjust test cases to reflect these changes so the testing process remains aligned with the current state of the software.

Visual analysis

AI-powered visual analysis enables engineers to identify issues and gaps in test coverage by interpreting the product user interface. AI algorithms can scan and analyze screenshots, videos, or live application feeds, detecting visual anomalies, layout inconsistencies, and functional issues. This approach allows for a more comprehensive user experience evaluation, ensuring that the software meets the expected visual and functional standards.

Using AI for visual analysis lets engineers uncover defects that traditional testing methods might miss. AI can identify issues such as incorrect alignments, color discrepancies, font inconsistencies, and broken layouts, which can negatively impact the user experience. For example, if a submit button is no longer visible on a form, an AI tool can visually assess the page, see the issue, and notify a developer.

AI-driven visual analysis can also help identify gaps in test coverage by highlighting areas of the user interface that still need to be thoroughly tested. This insight allows teams to optimize their testing efforts and ensure that all critical aspects of the application are adequately covered.

Real-time testing at scale

AI-driven testing enables engineers to perform testing tasks across the entire application in real time at a scale that human teams cannot match. Leveraging AI algorithms enables rapid testing, facilitating more frequent and comprehensive testing cycles. This approach continuously evaluates the application, reducing the risk of defects and improving overall software quality.

AI can analyze large quantities of user data generated during testing, identifying real-time patterns, trends, and anomalies. Using this analysis, testing efforts can be automatically prioritized based on the risk and impact of potential defects, ensuring that the most critical functionality receives immediate attention. AI also automatically adapts testing strategies to evaluate all changes thoroughly. This real-time adaptability allows teams to maintain high levels of software quality, even in the face of constantly changing requirements and tight deadlines.

{{banner-small-1="/banners"}}

Anomaly detection

AI-powered anomaly detection enables engineers to automatically identify potential defects within the product by interpreting test results. AI algorithms analyze the vast quantities of data generated during testing, including logs, metrics, and performance indicators, to detect patterns and anomalies that may indicate the presence of a defect. This automated approach to anomaly detection reduces the need for manual analysis, saving time and effort for the testing team.

This dashboard provided by the AI testing tool Qualiti showcases an excellent example of anomaly detection:

The tool can capture the most common errors and present them in an actionable manner for engineers. It can also show the progress on test coverage and the completeness of the test suite based on the data the AI has collected.

AI can learn from historical test data and establish baselines for normal application behavior. By comparing current test results against these baselines, AI tools can quickly identify deviations and flag them as potential anomalies. This approach allows for the early detection of defects, even in complex and large-scale applications. Additionally, tools can correlate anomalies across multiple test runs and data sources, providing a more comprehensive view of the application's health and helping pinpoint the defects' root causes.

Another neat feature of leveraging machine learning in testing is the ability to continuously monitor the application's performance and behavior, enabling the detection of defects that may emerge over time or under specific conditions. This proactive approach to anomaly detection ensures that potential issues are identified and addressed before they impact end-users, ultimately leading to higher-quality software and improved customer satisfaction.

Living test suites

AI can effectively manage test suites by continuously optimizing and refining the collection of test cases. Through analyzing user behavior and application changes, AI algorithms can identify redundant or outdated test cases and remove them from the test suite. This process ensures that the test suite remains lean and focused, testing only the user paths that are currently relevant. 

As a result, the test suite becomes a living entity that evolves alongside the application, always reflecting the most up-to-date user behavior and requirements. AI can generate and maintain living documentation based on the test suite, clearly and accurately describing the application's functionality and expected behavior.

Here are some of the key advantages of using a test suite as documentation:

• Single source of truth: The tests always match the documentation, so everyone on the team has the most current information about product functionality.
• Easier onboarding: Newer team members can use the tests to understand common user journeys and product features.
• Lower costs and saves time: Having AI maintain test cases as documentation saves developers and testers time by allowing them to focus on feature delivery and development instead of manually adding and updating documentation.

By utilizing AI to manage test suites and create living documentation, engineers can ensure that their testing efforts remain efficient, effective, and well-documented.

Test coverage within UI testing

AI can significantly enhance test coverage analysis and results in front-end web applications by collecting and interpreting data from user interactions and application behavior. AI algorithms can identify the most critical and frequently used UI components and user flows by monitoring and analyzing user actions, such as clicks, keystrokes, and navigation patterns. This data-driven approach enables engineers to prioritize testing efforts and ensure that the most critical aspects of the application receive adequate test coverage.

AI-powered test coverage analysis can provide valuable insights into the effectiveness of existing test cases and highlight areas that require additional testing. By comparing the actual user behavior with the test cases, AI can identify gaps in test coverage and suggest new test scenarios to fill those gaps. This iterative process of data collection, analysis, and test case optimization helps to improve the overall quality and reliability of front-end web applications. Furthermore, AI can generate visual reports and dashboards that present test coverage results in an easily understandable format, enabling developers and stakeholders to make informed decisions about application quality and prioritize improvement efforts.

{{banner-small-2="/banners"}}

Last thoughts

AI-driven testing represents a significant advancement in software quality assurance, addressing the limitations of traditional testing methods and empowering teams to deliver higher-quality applications in less time. By leveraging advanced algorithms, machine learning, and data analysis, AI-powered tools and techniques provide a comprehensive and adaptive approach to testing that ensures optimal test coverage, efficient defect detection, and living documentation. As software development continues to evolve, embracing AI-driven testing will be crucial for organizations looking to stay competitive and deliver exceptional user experiences.