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5 Modern Testing Techniques Delivering Value

What started as a breeze into the face of professional software testers has quickly become a headwind. Artificial intelligence and machine learning technology has become more sophisticated every day, and they are poised to completely change software testing. 

Fast release schedules and an emphasis on digital quality — not always complementary concepts — are pushing the agenda for AI-infused software development and testing. QA teams must change behaviorally and adopt modern AI and ML techniques to maximize testing value. When implemented correctly, AI and ML algorithms can help cut through complexity and adapt to changes as they’re made to requirements. Traditional testing techniques typically miss defects in obscure scenarios that humans overlook, but AI can catch some of these edge cases and blind spots. AI fundamentally changes how QA teams think about testing and reshapes future testing tasks and roles. 

This blog discusses five modern testing techniques with the greatest value for improving test efficiency, accuracy, coverage and speed, without requiring additional resources. 

#1. Intelligent test case generation

Many software testing teams create test cases using AI and ML technology. In these instances, AI and ML algorithms analyze application functionality and test data, then identify patterns and use them to generate test cases focused on identifying defects. AI and ML can develop tests that cover more scenarios, including those that testers miss. Even experienced testers can overlook or miss complex scenarios.

Take time to review the tests and make any necessary corrections. Reviewing and editing generated test cases still takes less time than creating them from scratch. 

Using AI/ML for intelligent test generation improves QA testing for:

  • increasing efficiency by reducing manual labor
  • boosting test coverage by generating comprehensive test case suites
  • improving test accuracy by allowing algorithms to identify hidden patterns and   non-documented relationships
  • cultivating adaptability 

Intelligent test case generation fits into any SDLC. Test cases can generate whenever there are existing user stories, requirements or code. If significant changes occur, generate the tests again to pick up changes. 

#2. Visual testing 

Visual regression testing validates that the visual elements within the UI remain accessible and visible in different user scenarios. Visual regression, or visual testing, ensures that all supported browsers and display sizes function properly and verifies consistent application usability. 

It sounds simple enough, but visual testing between various browsers, screen orientations and display settings takes a significant amount of testing effort. Consider this example:

  •     5 operating systems (Windows, MacOS, Android, iOS and Chrome)
  •     5 browsers
  •     2 screen orientations (portrait, landscape)
  •     18 standard laptop displays

Do the math, and you’ll come up with approximately 800 unique test combinations for one page. Add in testing mobile apps in native (iOS and Android) vs. hybrid models, and you’ve more than doubled the number of tests that need to be executed for every page in the app. 

Leveraging AI/ML within testing tools can automate visual testing and save testers significant time. Tests will automatically flag defects or changes to the page layout or elements within the page. Tests can run in parallel for different devices, platforms and browsers. 

When you can effectively and efficiently automate visual testing, you enable testers to focus on high-risk testing that requires human intervention. The number of defects found will increase, preventing them from reaching customers. 

Visual testing, when done manually, is performed at the end of the SDLC and before release. By leveraging AI tools and automation, visual testing fits in the SDLC at the beginning, middle and end. The flexibility of test execution means there’s no need to wait. Testing gets scheduled as needed.

Keep in mind, however, that testing with real users in a crowdtesting approach will still yield a more accurate view of these user experiences, as it enables you to test on real devices, browsers, OSes and orientations in real scenarios.

#3. Test data generation

Generating test data has been a challenge since the dawn of software testing as a practice. Test data must match live or production data types to best mimic how the application manages data in production. QA teams rely on development to create simulated connections to test effectively on test servers.

Teams generate test data multiple times for multiple test servers, and refresh or reset them frequently. However, it’s difficult to manually generate data or convince development to invest the time into creating scripts.

AI/ML tools can generate fairly accurate and high-quality test data without imposing on developers when new data is needed. AI-enabled test data generation saves time and increases the speed and accuracy of testing while also ensuring data security. Many test data generation tools support Cartesian product and combinatorial test design (CTD). CTD ensures test data includes corner cases and non-happy path data models. Test data generation tools also create non-confidential data that reduces the risk of data compliance challenges and breaches.

#4. Defect prediction

Software defect prediction (SDP) significantly benefits software testing teams through predictive AI/ML. Using defect prediction, testing teams reduce costs by using data to analyze where the greatest number of defects occur in the application. Essentially, using AI/ML for SDP enables testers to automatically generate testing priority and ensure test execution is focused on defect-prone areas of the application. 

SDP systems use the learning-to-rank (LTR) algorithm along with support vector (SVM), random forest (RF), and k-nearest neighbor (KNN) to predict and prioritize testing areas based on the number of predicted defects in each functional area of an application. 

SDP tools are useful throughout the SDLC, from design to release. When the team needs to focus on high-priority tests in a short window of time, consider SDP to help analyze the application data as well as identify and prioritize areas of the code that are prone to defects. 

#5. Automated test development and maintenance

The most popular use of AI/ML technology in software testing is in automated test development and maintenance. Taking a cue from the first section, which focuses on test case generation, this section involves the creation and maintenance of automated test scripts.

Advantages of AI/ML for test automation include:

  • automated test generation for all scenarios, including edge cases
  • assistance with automated test case development by providing suggestions for improvement
  • increased test coverage by enabling test teams to create tests for previously uncovered areas in QA, like security or performance.
  • reduced impact of test maintenance on rapid-release cycles.

AI/ML tools significantly improve the bottleneck of automated script maintenance. Many tools can repair scripts automatically or can be used to provide suggestions for edits. AI/ML tools also help with failure analysis, which determines if an error is a defect or a script problem — a process that normally takes a considerable amount of resource time. Leverage AI/ML to reduce time spent on failure analysis and script maintenance. 

Using automated testing tools with built-in AI/ML also improves test accuracy. Algorithms can directly uncover hidden patterns and relationships in the code, boosting test accuracy and reducing missed test cases due to testers’ lack of awareness of hidden functions within the code base. 

AI/ML in test automation also improves the ability of test development to adapt quickly to changes in requirements. Using AI/ML tools makes continuous testing more successful by easing the time-consuming burden of test development, execution, maintenance and failure analysis. 

The flexibility AI/ML provides makes automated test development a possibility anywhere in the SDLC. Effective test automation at the beginning of the SDLC ensures better script quality and coverage at release. 

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The future of software testing

Internal and crowdsourced software testers will remain in demand as long as humans use applications and demand a quality experience. These approaches retain their significant business value because they protect customers — and, just as important, the company brand — from bad user experiences. Positive user experiences build trust in the application as well as the provider. Building trust by deploying well-tested and high-quality applications means happier customers who tell the world about their experiences. 

Trust is also essential between the AI/ML technology and testing teams. The technology is young and needs improvement before AI/ML becomes trustworthy on a wide basis. Software testing teams might become the auditors of AI/ML. Instead of test development and execution, the human testing role may require auditing and reviewing to ensure the validity of the test effort. 

Use AI/ML technology and tools in coordination with internal and crowdsourced testing resources to improve test accuracy and efficiency. Plan time to review and provide human oversight to all AI/ML actions until trust is established and retained.

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Published: June 27, 2024
Reading Time: 10 min

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