What once began as a gentle stream guiding software testers, has transformed into a rushing river of challenges. With each passing day AI and ML have become more sophisticated and are set on a path to revolutionize the world of software testing. With the rapid release schedules and the ever-evolving emphasis on the digital quality of software, the need for AI-integrated software development and testing is at an all-time high.  

To keep up with the needs and requirements, it is integral that QA teams change and adopt next-gen techniques to enhance the value delivered through testing. When utilized efficiently, AI and ML algorithms are known to decrease complexity and align with the evolving requirements. There is excessive scope for error with traditional testing techniques, which typically includes missing defects in obscure scenarios due to human error. In such situations, AI can easily detect some of those edge cases and see through those missed blind spots. AI has the potential to fundamentally change the way QA teams think about testing and can reshape the future of software testing roles and tasks.  

Discover the top 5 modern testing techniques to improve the efficiency, accuracy, coverage and speed of your software testing without any additional tools by reading this blog further.  

1. Automated test case creation  

Many software testing teams now use AI and ML to create test cases. These tools analyze software and data to automatically generate tests that find defects. This approach often surpasses human capabilities in identifying complex issues.  While human review is essential, editing AI-generated test cases is quicker than creating them from scratch. The 2023 State of Testing report by Practitest revealed that the vast majority of companies (93%) leverage automated tools to generate at least some of their test cases. The extensive adoption of test automation can be attributed to its many advantages.  

  • Accelerated testing cycles: Reduced manual effort leads to faster time-to-market. 
  • Expanded test coverage: Comprehensive test suites are generated, including edge cases. 
  • Improved test effectiveness: Algorithms uncover hidden patterns and anomalies. 
  • Adaptability: Test cases can be regenerated to align with evolving requirements. 

AI-powered test case generation can be used throughout the software development process, from initial planning to final testing.  

2. Visual regression testing  

Visual testing verifies that your application’s appearance is consistent across different platforms and screen sizes. This includes ensuring that all visual elements are clear and accessible to users. Visual testing is integral to retain customers because: 

visual regression testing

While visual testing might seem straightforward, the reality is complex. Consider the challenge of testing across different browsers, screen sizes, and orientations. Some of which are: 

  • Operating Systems: Windows, macOS, Android, iOS, Chrome OS 
  • Browsers: A variety of browsers (e.g., Chrome, Firefox, Safari, Edge) 
  • Screen Orientations: Portrait and landscape modes 
  • Display Sizes: A range of standard laptop display resolutions 

When considering the various permutations of operating systems, browsers, orientations, and display sizes, it becomes clear that testing a single page involves managing approximately 800 test cases. The inclusion of mobile app testing, with its native and hybrid variations, exponentially expands this number.

AI and ML-powered testing tools streamline visual testing by automating the process. These tools can independently identify visual defects and inconsistencies across different devices and browsers, significantly reducing manual testing efforts. Automating visual testing frees testers to prioritize high-impact testing activities. This shift in focus results in a higher defect detection rate and ultimately aids in delivering a superior product.  

Traditionally, visual testing was a manual process conducted late in the development cycle. With AI-driven automation, visual testing can be integrated throughout the entire development process, from initial design to final release. This flexibility allows for continuous testing and immediate feedback. While automation is invaluable, real-user testing remains crucial for capturing authentic user experiences across various devices and conditions.  

3. Synthetic data generation  

Creating realistic test data has been a persistent challenge for software testers. To accurately simulate real-world conditions, test data must closely resemble production data regarding structure and content. Historically, QA teams have relied on development resources to establish simulated environments for effective testing. Test teams often find themselves repeatedly generating test data for various test environments. This data requires frequent updates or resets. Manual data creation is time-consuming and resource-intensive. Moreover, convincing development teams to dedicate time to scripting data generation processes can be challenging. 

AI and ML technologies have transformed test data generation by producing accurate, high-quality data without relying on developer resources. By automating the generation of accurate and high-quality test data, these tools significantly accelerate testing cycles, reduce reliance on development resources, and mitigate data privacy risks. Many test data generation tools incorporate advanced techniques like Cartesian product and combinatorial test design to cover a wide range of test scenarios, including edge cases. By generating synthetic, non-sensitive data, these tools mitigate compliance risks. 

The potential of this technique is apparent with the statistics from the global synthetic data generation market, valued at USD 316.11 million in 2023, is projected to expand at a CAGR of 34.8% from 2024 to 2033. 

4. Proactive defect identification  

Software Defect Prediction (SDP) is a critical component of modern quality assurance, leveraging AI and ML to proactively identify potential defects within software applications. By analyzing historical defect data, SDP models can predict high-risk areas, enabling testing teams to optimize resource allocation and prioritize test execution accordingly. This data-driven approach significantly reduces testing costs and enhances overall software quality. 

SDP systems employ advanced algorithms such as learning-to-rank, support vector machines, random forest, and k-nearest neighbors to generate precise defect predictions. These models facilitate the creation of prioritized test suites, ensuring that testing efforts are focused on areas with the highest likelihood of defects. The application of SDP throughout the software development lifecycle, from design to release, empowers teams to make informed decisions and mitigate risks effectively. 

5. Test automation lifecycle management  

The integration of AI and machine learning is reshaping the software testing landscape. The AI-enabled testing market, currently valued at $426.1 million, is projected to have a growth value of $2 billion by 2033 highlights the industry’s belief in AI’s power to revolutionize testing. AI and ML are instrumental in the development and maintenance of automated test suites, building upon their role in test case generation.  

AI is significantly advancing automated testing capabilities. Beyond test case creation, AI optimizes test script maintenance and execution. By intelligently analyzing test results and code, AI-driven tools autonomously identify and rectify test failures, enhancing efficiency and effectiveness. Moreover, AI’s ability to uncover hidden code patterns improves test coverage and software quality.  

 Conclusion 

Human testers will continue to play a vital role in ensuring quality user experiences, despite advancements in AI and ML. Their expertise is crucial in safeguarding both customer satisfaction and brand reputation. While AI and ML offer significant benefits, human oversight remains essential to maintain trust and reliability in testing processes.  

By incorporating these modern testing techniques, QA teams can significantly enhance software quality and efficiency. Automated test case creation, visual regression testing, synthetic data generation, defect prediction, and test automation lifecycle management are essential components of a successful testing strategy. To fully realize the benefits of these advancements, consider exploring how AI and ML can be integrated into your testing processes.  

Latest posts by Harshavardini Murali (see all)