List comprehensions are a Pythonic functional construct allowing developers to express in a concise way loops to build and manipulate lists. Previous studies point to a gain in speed when list comprehensions are adopted. This paper reports the results of a study that compares the execution time performance of Python code written using list comprehensions as opposed to equivalent imperative programming. To this aim, we have developed a set of transformation rules to map Python for loops into list comprehensions. On the one hand, on artificial code snippets, we found list comprehensions to be faster than procedural code, with differences becoming evident if amplifying the tests, i.e., executing the code fragment thousands of times. On the other hand, this does not happen when executing real-world Python projects, where the performance may or may not improve, depending on the projects' features and the nature of the manipulated objects.

Functional programming is expected to introduce several benefits to programs, including fewer side effects, easier parallelization, and even, in some circumstances, better comprehensibility. This paper investigates the extent to which the addition/modification of certain programming language constructs, i.e., lambdas, comprehensions, and map/filter/reduce, have higher chances to induce fixes than other changes. To this extent, we analyze the change history of 200 popular open-source programs written in Python, accounting for ≃ 630k commits and 6M changes. The study results show that changes to functional constructs have higher odds to induce fixes than other changes, and that some functional constructs, such as lambdas and comprehensions, have higher odds to induce fixes than others. Finally, a qualitative analysis revealed different scenarios in which functional constructs have been fixed. Results of this study may trigger better development support when using functional constructs during development, and prioritize code review and testing on certain areas of the source code.