Lint-Based Warnings in Python Code: Frequency, Awareness and Refactoring

Abstract

Python is a popular programming language characterized by its simple syntax and easy learning curve. Like many languages, Python has a set of best practices that should be followed to avoid bugs and improve other quality attributes (such as maintenance and readability). In this context, non-compliance to these practices can be detected by using linting tools. Previous work conducted studies to better understand the frequency of a class of problems that can be found using Python linters: warnings, here named as lint-based warnings. However, they either rely on small datasets or focus on few domains, such as machine learning or web-systems projects. In this paper, we provide a mixed-method study where we analyze the frequency of six lint-based warnings in 1,119 different open-source general-purpose Python projects. To go further, we also conduct a survey to check whether developers are aware of the lint-based warnings we study here. In particular, we intend to check whether they are able to identify the six lint-based warnings. To remove the lint-based warnings, we suggest the application of simple refactorings. Last but not least, we evaluate the suggestions by submitting pull requests to remove lint-based warnings from open-source projects. Our results show that 39% of the 1,119 projects have at least one lint-based warning. After analyzing the survey data, we also show that developers prefer Python code without lint-based warnings. Regarding the pull requests, we achieve a 71.8% of acceptance rate.