On the applicability of Artificial Intelligence in Black Box Testing (Khanna, 2017) might be a good place to start. It classifies some black box testing techniques based on the AI branch it belongs to.


In a simple linear model of the form $y = \beta_0 + \beta_1 x $ we can see that increasing $x$ by a unit will increase the prediction on $y$ by $\beta_1$. Here we can completely determine what the effect on the models prediction will be by increasing $x$. With more complex models such as neural networks it is much more difficult to tell due to all the ...

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