  Automated unit test generation is an established research field, and mature
test generation tools exist for statically typed programming languages such as
Java. It is, however, substantially more difficult to automatically generate
supportive tests for dynamically typed programming languages such as Python,
due to the lack of type information and the dynamic nature of the language. In
this paper, we describe a foray into the problem of unit test generation for
dynamically typed languages. We introduce Pynguin, an automated unit test
generation framework for Python. Using Pynguin, we aim to empirically shed
light on two central questions: (1) Do well-established search-based test
generation methods, previously evaluated only on statically typed languages,
generalise to dynamically typed languages? (2) What is the influence of
incomplete type information and dynamic typing on the problem of automated test
generation? Our experiments confirm that evolutionary algorithms can outperform
random test generation also in the context of Python, and can even alleviate
the problem of absent type information to some degree. However, our results
demonstrate that dynamic typing nevertheless poses a fundamental issue for test
generation, suggesting future work on integrating type inference.
