This article predominantly talks about testing as a distinct career option in data science and machine learning (ML). It gives a brief on testing workflows and process. It also depicts the expertise and top-level skills a tester needs to possess in order to test a ML application. There is a significant opportunity to explore and expand the possibilities of testing and quality assurance into the field of data science and machine learning (ML). Playing around with training data, algorithms and modeling in data science may be a complex yet interesting activity--but testing these applications is no less.

A very popular notion in quality management is the notion of Poka-Yoke. It is invented in Japan for ensuring quality. Poka-Yoke means'mistake-proofing' or more literally -- avoiding (yokeru) inadvertent errors (poka). In context of our daily lives there are several instances of Poka-Yoke in action. To consider the popular example, let us see what happens when you try to enter an elevator suddenly, the sensor in the doors detects your presence and causes the doors to open.

This paper provides a comprehensive survey of Machine Learning Testing (ML testing) research. It covers 128 papers on testing properties (e.g., correctness, robustness, and fairness), testing components (e.g., the data, learning program, and framework), testing workflow (e.g., test generation and test evaluation), and application scenarios (e.g., autonomous driving, machine translation). The paper also analyses trends concerning datasets, research trends, and research focus, concluding with research challenges and promising research directions in ML testing.