Test Intelligence in the Era of AI: Opportunities and Challenges

The software development field currently represents a significant target for artificial intelligence (AI) and machine learning (ML) technology. Why software development? Software development and testing are fields where AI/ML-driven technology is used because software applications are increasingly complex and data-intensive.

For QA testing teams, test intelligence is more than experience. It represents the benefits of AI/ML in improving software’s testing intelligence from humans and technology. Modern testing requires faster execution speeds with increasingly complex systems and frequent code releases to production. AI/ML technology represents a saving grace for software testing effectiveness and a source of new opportunities for testers.

This guide describes what test intelligence means for modern testing teams in the AI/ML technology era, including the benefits, challenges, and opportunities AI creates. It will include the following topics:

• What is test intelligence?
• What are the benefits of using AI/ML technology for testing?
• What are typical challenges for testing teams using AI/ML technology?
• What opportunities exist for testers in the AI era?

What is Test Intelligence?

Test intelligence means planning and conducting software testing using humans and AI/ML in an organized, proactive, and rapid manner. The result is the delivery of high-quality software applications for customers. Proactive testing is essential for meeting the business and application needs of development organizations and customers.

Test intelligence presents for both human and AI/ML technology. Each type of test intelligence functions best in tandem. AI/ML technology improves testing but requires management to ensure testing continues to be valid, thorough, and accurate for a wide variety of users.

Test intelligence starts with test planning during a sprint for thorough and accurate coverage that identifies issues early in the software development lifecycle (SDLC). Intelligent testing may be as simple as preventing duplicate work, re-work, or overlaps in test execution. It creates effective and efficient testing that provides extended test coverage within short iteration cycles.

AI/ML technology in software testing is positively changing and providing numerous opportunities for QA testers and test teams. Combining the test intelligence of human testers and AI/ML technology is currently causing significant changes and improvements in testing quality, speed, and effectiveness.

For instance, LambdaTest Test Intelligence combines cutting-edge AI/ML with human expertise to transform software testing, ensuring faster, more accurate, and comprehensive coverage throughout the development lifecycle.

Benefits of Using AI/ML Intelligence for Testing

The impact and benefits of software testing using humans and AI/ML technology are only beginning. The resulting change to application quality will be seen in the long term over the next decade. Testing teams should get involved early so they can participate in the future direction of testing and quality customer experience.

Benefits of integrating AI/ML technology for testing include:

• Improved speed and efficiency
• Testing accuracy and increased precision
• Increased test flexibility with reduced cost
• Test scalability at speed
• Increased use of quality and secure data
• Improved risk management during iterations
• Better defect detection with fewer bugs pushed to customers
• Increased test coverage

Testing speed and overall efficiency improve when QA processes are lean and use automated processes that reduce manual workloads. For example, AI/ML technology enables parallel server testing and supports cross-browser testing. More testing gets done, and that testing is accurate and focused on areas in the code that have changed. AI/ML can assist testing teams in identifying and prioritizing testing that improves the user experience based on accumulated data and history.

AI/ML testing tools scale faster and up and down. In other words, testing teams can plan and execute tests according to the application users and the application’s inherent complexity or simplicity. Testers can create load and performance testing without the need to create a new team project or developer assistance. The QA team uses AI/ML technology to provide additional coverage when needed.

AI/ML technology works off of data. Human testers also work off data accumulated from years of application testing experience. The difference is that human memory can be fallible. AI/ML data-driven testing is easier to access, copy, refresh, and update. Human experience and AL/ML data are the best combination for improving customer experience.

AI/ML Testing Methods

The benefits of AI/ML technology in testing result from using automated test case generation, intelligent test prioritization, predictive defect detection, and test automation tool improvements. Test automation improvements include predictive maintenance, script assistance, and true support for continuous testing for continuous integration and deployment (CI/CD).

AI/ML technology provides methods of automatically generating user-centric tests as well as tests for APIs, data connectivity, background runtime engines, and hidden processes. AI/ML helps testers by opening up the black box and enabling testers to build tests that focus on the UI and user experience, as well as testing backend processes and integrated third-party applications.

Testing teams save significant time by not creating automated test scripts or writing manual test cases. AI/ML also knows the edge cases so test scenarios are not missed. ML also learns over time and includes testing for security vulnerabilities and boundary conditions.

The intelligent test prioritization method uses AI/ML to rank tests according to their ability to expose critical defects. ML models and algorithms use historical data from defect reports and testing history to find patterns and trends and match them with code modifications and fault incidence. Pair test prioritization by AI/ML with human tester experience, and test coverage accuracy increases. Test what needs testing rather than repeating unnecessary testing.

Defect or anomaly detection helps testing teams eliminate being blindsided by errors from system breakdowns, performance degradation, and security issues. Each of these areas poses a significant risk to application quality and user experience. Defects in these areas usually go undetected until the code is released to production. AI/ML technology enables testing teams to identify defects like these early in and throughout the development cycle. For example, deep learning models similar to convolutional neural networks (CNNs) and recurrent neural networks (RNNs) make real-time defect detection possible by scanning spatial relationships throughout the data and flagging issues for human review.

Testing teams that use test automation tools with AI/ML technology can now realistically meet test automation demands for providing consistent and autonomous testing that runs 24/7 and is valuable for CI/CD and continuous testing needs. Modern AI/ML test automation tools enable cross-platform and parallel testing across test servers. Additionally, they offer automatic test generation, scripting assistance, and predictive or self-healing options to minimize the impact of test maintenance.

AI/ML Integration Challenges for QA Testers

Integrating new technology is always challenging. AI/ML comes in a wide variety of tools and options. Create a test strategy that includes working AI/ML into existing testing processes or re-write existing testing processes to include working with AI/ML. The best way to prepare for an industry-altering change is to understand it as it evolves. Get the testing team trained on AI/ML tools and find ways to use AI/ML to your advantage.

Learning new technology as one works can be challenging and distracting. Plan time to introduce new tools and processes gradually over time. Consider starting with an AI/ML test automation tool or using AI to generate test data across test servers. Many teams may also consider using AI/ML for test case development for new projects.

The biggest challenge software development teams experience using AI/ML is fully understanding the importance of data quality. If the AI/ML is learning from bad or inaccurate data sets, then the test cases generated may be invalid or contain omissions or bias. Teach human testers to review the AI/ML work and note any inaccuracies. Human review is essential at the current AI/ML stage.

Opportunities for QA Testers

Many challenges of using AI/ML technology in testing also present opportunities for QA testers and testing teams to improve testing quality. Consider the opportunity to learn how to review AI/ML-generated test cases or scripts for accuracy. A new software testing role may eventually include a role like a QA Auditor, who may be responsible for verifying the accuracy of generated tests.

Another opportunity is learning to use AI/ML technology to provide a better overall client experience from the beginning of development to customer release and use. Increasing test coverage combined with improved accuracy and prioritization helps reduce defects. Another is the ability to perform security scans and testing during the development cycle rather than at the end or post-release. Using AI/ML to test performance, load, and security goes a long way to improving application quality.

Additional QA tester opportunities with AI include:

• Developing and ensuring ethical AI testing practices
• Ensuring AI tests meet regulatory standards when necessary
• Experience using Quantum computing to test even the most intricate and complex scenarios in seconds
• Learn how to leverage AI/ML for cognitive testing for increased usability
• Using AI/ML to perform exploratory testing routinely and identify defects
• Creating reports on demand within seconds
• Enhancing data security and protection from threats by running security tests with AI/ML input and scanning abilities
• Develop expertise in using AI/ML test automation tools that provide a foundation for creating autonomous testing

Conclusion

AI/ML technology is changing software testing and development. It’ll continue to evolve and impact the testing profession for years or decades. However, remember that new technology makes great promises, with only a few that usually meet expectations. Don’t give up on your QA testing career because there’s plenty of testing to do. Be prepared to learn and become adaptable, flexible, and creative.

Unlock smarter testing with LambdaTest Test Intelligence. Seamlessly blend AI/ML technology with human insight for faster, more accurate results. Get started today!

Amy E Reichert

Amy E Reichert is a Freelance writer for a variety of topics focused mainly on QA testing, Agile, and technology trends. Amy has 23 years of professional experience as a QA Engineer/Analyst within the ERP, healthcare, and business management sectors. Many years of developing test process, leading diverse and inclusive teams as well as testing on mobile and web applications.

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