Organizations have tried to speed up and optimize software testing processes with continuous releases of high-quality software but with the evolution of Artificial Intelligence (AI) enabled solutions, testing has become more challenging due to the complexity involved in testing AI systems. According to a research report, it is expected that by 2025, the value of the AI market is said to surpass US $100B.
Specifically, AI- based systems testing is essentially difficult as different input and output combinations that are fed to the system should be tested which is more towards a non-deterministic approach. Moreover, as the world is increasingly moving towards increased adoption of AI-powered smart applications, there is every need for end-to-end AI systems testing to ensure fully functional and high-performing AI systems. Hence, in order to ensure effective AI testing, TestingXperts follows an effective, and comprehensive testing strategy for testing AI systems.
A/B testing | • Classic AB Test, Split tests, and MVT to compare variations of features |
API testing | • Understand API endpoints between UI, NLP, and data store |
Non-functional testing | • Non-functional requirements like performance, security, usability, and accessibility |
Input Data testing | • Different kinds of input values to test expected and unexpected behavior |
UX testing | • Perform interoperability testing with multiple devices • Perform user experience and accessibility testing |
ML testing | • Spoon-feed the AI with specific data to test the change in its behavior |
Voice and NLP text testing | • Provide user input in the form of voice or text. • Verify its capabilities to process intent and respond with the appropriate utterances |