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Node.js programs are invoked by running the interpreter node interpreter with a given file, so the first two arguments will be node and the name of the JavaScript source file. It is often useful to extract the rest of the arguments by slicing a sub-array from process.argv .
Mock objects have the same interface as the real objects they mimic, allowing a client object to remain unaware of whether it is using a real object or a mock object. Many available mock object frameworks allow the programmer to specify which methods will be invoked on a mock object, in what order, what parameters will be passed to them, and what values will be returned.
A function call using named parameters differs from a regular function call in that the arguments are passed by associating each one with a parameter name, instead of providing an ordered list of arguments. For example, consider this Java or C# method call that doesn't use named parameters:
The tools listed here support emulating [1] or simulating APIs and software systems.They are also called [2] API mocking tools, service virtualization tools, over the wire test doubles and tools for stubbing and mocking HTTP(S) and other protocols. [1]
Mock objects differ in that they themselves contain test assertions that can make the test fail, for example, if the person's name and other data are not as expected. Fake and mock object methods that return data, ostensibly from a data store or user, can help the test process by always returning the same, realistic data that tests can rely upon.
Python's name is derived from the British comedy group Monty Python, whom Python creator Guido van Rossum enjoyed while developing the language. Monty Python references appear frequently in Python code and culture; [190] for example, the metasyntactic variables often used in Python literature are spam and eggs instead of the traditional foo and ...
Python sets are very much like mathematical sets, and support operations like set intersection and union. Python also features a frozenset class for immutable sets, see Collection types. Dictionaries (class dict) are mutable mappings tying keys and corresponding values. Python has special syntax to create dictionaries ({key: value})
In case of call by value, what is passed to the function is the value of the argument – for example, f(2) and a = 2; f(a) are equivalent calls – while in call by reference, with a variable as argument, what is passed is a reference to that variable - even though the syntax for the function call could stay the same. [5]