# Development Hints¶

## Deterministic behavior¶

Pynguin’s behavior is inherently probabilistic. However, in order to easily reproduce an execution, e.g. for an evaluation or for debugging purposes, it must be possible to achieve deterministic behavior.

For this purpose, whenever an implementation uses randomness, it must be done via pynguin.utils.randomness. This module contains a singleton instance of a (pseudo) random-number generator that is seeded at startup (using --seed).

Furthermore, when an implementation’s behavior depends on the iteration order of a data structure, e.g., when picking a random element from it, one has to ensure that this order is deterministic:

• lists and tuples are ordered by design.

• dicts store their insertion order beginning with Python 3.7 and are therefore safe to use.

• Python’s builtin sets do not guarantee any order, thus one has to use OrderedSet, which is a set implementation that stores the insertion order of its elements.

## Overriding __eq__ and __hash__ methods¶

Similar to the Java world, we enforce to adhere to contracts for equals and hash methods as described, for example, in the Java API documentation. More information can also be found in Joshua Bloch’s famous book Effective Java. In particular, when you override the __eq__ or the __hash__ method of a class you are also required to override its opponent.

The following contracts should hold (adopted from the Java API documentation):

1. For __hash__

• __hash__ must consistently return the same integer whenever it is invoked on the same object more than once during one execution of the Python application, provided no information used in __eq__ comparisons on the same object is modified. The integer need not remain consistent from one application execution to another execution of the same application.

• If two objects are equal according to the __eq__ method then the __hash__ value of the two objects must be the same

• It is not required that for two objects being unequal according to the __eq__ method, the __hash__ value must be distinct, although this could improve performance of hash tables.

1. For __eq__

• The relation is reflexive: for any non-null reference value x, x.__eq__(x) should return True

• The relation is symmetric: for any non-null reference values x and y, x.__eq__(y) should return True if and only if y.__eq__(x) returns True.

• The relation is transitive: for any non-null reference values x, y, and z, if x.__eq__(y) returns True and y.__eq__(z) returns True, then also x.__eq__(z) should return True.

• The relation is consistent: Multiple invocations of the method on the same two objects should yield the same result as long as none of the objects has been changed.

• For any non-null reference value x, x.__eq__(None) should return False

## Overriding __str__ and __repr__ methods¶

The goal of a __str__ method is to provide a string representation that is usable and readable for a user. The goal of the __repr__ method is to be unambiguous, see StackOverflow. We encourage you to provide a __repr__ representation that looks like the Python code that creates an object with the state of the object __repr__ was called on. Consider the following example:

 1class Example:
2    def __init__(
3        self, foo: str, bar: int, baz: List[str]
4    ) -> None:
5        self._foo = foo
6        self._bar = bar
7        self._baz = baz
8
9
10example = Example("abc", 42, ["xyz", "pynguin"])


The representation, i.e., the result yielded by calling __repr__ on the example object should look like

Example(foo="abc", bar=42, baz=["xyz", "pynguin"])


which can be achieved by implementing the __repr__ method of the Example class as follows:

1    def __repr__(self) -> str:
2        return f"Example(foo=\"{self._foo}\", bar={self._bar}, "
3               f"baz={repr(self._baz)})"


## Guarding imports for type checking¶

Some imports in a module are only necessary for type checking but not at runtime. We guard these imports by if typing.TYPE_CHECKING blocks. The main reason for this is to prevent circular imports. During type checking, these imports do not harm the type checker as it uses much more sophisticated techniques to handle the circular imports (like a compiler does) in contrast to the simple handling of the interpreter.

## Debugging test case execution¶

We execute test cases in a separate thread. To track data on the test case execution, e.g., line or branch coverage, we use thread-local storage. Usage of threading.local may interfere with debugging tools, such as pydevd. In such a case, disable Cython by setting the following environment variable: PYDEVD_USE_CYTHON=NO