An Empirical Study on the Impact of Python Dynamic Typing on the Project Maintenance

Python is a popular typical dynamic programming language. In Python, dynamic typing is one of the most critical dynamic features. The lack of type information is likely to hinder the maintenance of Python projects. However, existing work has seldom focused on studying the impact of Python dynamic typing on project maintenance. This paper focuses on the two most common practices of Python dynamic typing, i.e. inconsistent-type assignments (ITA) and inconsistent variable types (IVT). Two approaches are proposed to identify ITA and IVT, i.e. identifying ITA by analyzing Abstract Syntax Trees and comparing identifiers types and identifying IVT by constructing a type dependency graph. In empirical experiments, we first locate the usage of ITA and IVT in 10 open-source Python projects. Then, we investigate the relations between the occurrence of ITA and IVT and the results of maintenance tasks. The study results show that projects are more prone to change as the number of dynamic typing identifiers increases. There is a weak connection between change-proneness and variable dynamic typing. There is a high probability that maintenance time and the acceptance of commits decrease as dynamic typing identifiers increase in projects. These results implicate that dynamic and static variables should be divided while developing new programming languages. Dynamic typing identifiers may not be the direct root causes for most software bugs. The categories of these bugs are worth exploring.