An empirical study on the impact of Python dynamic features on change-proneness

The dynamic features of programming languages are useful constructs that bring developers convenience and flexibility, but they are also perceived to lead to difficulties in software maintenance. Figuring out whether the use of dynamic features affects maintenance is significant for both researchers and practitioners, yet little work has been done to investigate it. In this paper, we conduct an empirical study to explore whether program source code files using dynamic features are more change-prone and whether particular categories of dynamic features are more correlated to change-proneness than others. To this end, we statically analyze historical data from 4 to 7 years of the development of seven open-source systems. We employ Fisher and Mann-Whitney hypothetical test methods, along with logistic regression model to solve three research questions. The results show that: (1) files with dynamic features are more change-prone, (2) files with a higher number of dynamic features are more change-prone, and (3) Introspection is shown to be more correlated to change-proneness than the other three categories in most systems. This innovative work can give some inspirations and references to researchers who are always focusing their eyes on how and why the dynamic features are used. For practitioners, we suggest them to be wary of files with dynamic features because they are more likely to be the subject of their maintenance effort. Keywordsdynamic features; change-proneness; Python; empirical software engineering; open-source