On the Usefulness of Python Structural Pattern Matching: An Empirical Study

As the important role of software in our modern world becomes more and more evident, the need for more complex data structures is increasing. Structural pattern matching has become an elegant technique for simplifying complex conditionallogic in the source code in modern programming languages. It provides an easy and concise way to destructure complex data and enables making decisions based on its structure, ultimately improving code readability. In the context of Python, a language renowned for its simplicity, structural pattern matching was a missing feature until October 2021. Finally, the feature is shipped in Python 3.10, allowing developers to exploit the potential of structural pattern matching. Instead of traditional conditional branching and endless type checking, structural pattern matching offers a more straightforward way to handle data. In this paper, we investigate the usefulness of this relatively new Python feature by involving 65 participants in a code review experiment. The participants (coming from diverse programming backgrounds) were presented with pairs of code snippets (one using structural pattern matching while the other using traditional conditional branching), each addressing the same task. They had to choose which code they preferred using a 4- point Likert scale based on three criteria: readability, modifiability, and personal preference. In the vast majority of cases, developers preferred code with structural pattern matching, but there were certain contexts in which a significant proportion of developers preferred the original code.