The Complete Guide to Testing Types: Traditional vs AI Era
This article explores the evolution of software testing, covering traditional testing approaches and the emerging field of AI-powered testing.
Why it matters
As AI/ML systems become more prevalent, understanding the evolving testing landscape is crucial for ensuring the quality and reliability of these systems.
Key Points
- 1Traditional testing includes functional testing (unit, integration, system, acceptance, regression, smoke, sanity), non-functional testing (performance, security, usability, compatibility, reliability), and structural testing (white box, black box, gray box)
- 2AI/ML testing focuses on data testing (data quality, data validation, data drift), model testing (accuracy, performance, robustness, metamorphic), and end-to-end AI system testing
- 3AI systems require new testing approaches beyond just measuring model accuracy, such as testing for performance, robustness, and integration with other components
Details
The article provides a comprehensive overview of the testing landscape, covering both traditional software testing and the emerging field of AI-powered testing. It explains the different types of traditional testing, including functional testing (unit, integration, system, acceptance, regression, smoke, sanity), non-functional testing (performance, security, usability, compatibility, reliability), and structural testing (white box, black box, gray box). The article then delves into the unique challenges of testing AI/ML systems, highlighting the importance of data testing (data quality, data validation, data drift), model testing (accuracy, performance, robustness, metamorphic), and end-to-end AI system testing. The key message is that while traditional testing remains a foundation, the rise of AI/ML requires new testing approaches that go beyond just measuring model accuracy.
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