In the chapter on "Mutation-Based Fuzzing", we have seen how to use extra hints – such as sample input files – to speed up test generation. In this chapter, we take this idea one step further, by providing a specification of the legal inputs to a program. Specifying inputs via a grammar allows for very systematic and efficient test generation, in particular for complex input formats. Grammars also serve as the base for configuration fuzzing, API fuzzing, GUI fuzzing, and many more.Prereq...| www.fuzzingbook.org
Represents an HTTP request, which is usually generated in a Spider and| docs.scrapy.org
You know Python is essential for a data engineer. Does anyone know how much one should learn to become a data engineer? When you're in an interview with a hiring manager, how can you effectively demonstrate your Python proficiency? Imagine knowing exactly how to build resilient and stable data pipelines (using any language). Knowing the foundational ideas for data processing will ensure you can quickly adapt to the ever-changing tools landscape. In this post, we will review the concepts you n...| www.startdataengineering.com
(n,)#A parenthesized number followed by a comma denotes a tuple with one| numpy.org