An Empirical Investigation of Command-Line Customization

TL;DR

This paper analyzes over 2.2 million shell alias definitions from GitHub to understand command-line customization practices, revealing common patterns and their implications for usability and tool design.

Contribution

It introduces a comprehensive empirical analysis of shell alias types and practices, providing insights into user customization behaviors and usability issues.

Findings

Identified three main alias types: Shortcuts, Modifications, Scripts.

Revealed common customization practices and their potential usability implications.

Provided a curated dataset and notebooks for further research.

Abstract

The interactive command line, also known as the shell, is a prominent mechanism used extensively by a wide range of software professionals (engineers, system administrators, data scientists, etc.). Shell customizations can therefore provide insight into the tasks they repeatedly perform, how well the standard environment supports those tasks, and ways in which the environment could be productively extended or modified. To characterize the patterns and complexities of command-line customization, we mined the collective knowledge of command-line users by analyzing more than 2.2 million shell alias definitions found on GitHub. Shell aliases allow command-line users to customize their environment by defining arbitrarily complex command substitutions. Using inductive coding methods, we found three types of aliases that each enable a number of customization practices: Shortcuts (for nicknaming commands, abbreviating subcommands, and bookmarking locations), Modifications (for substituting commands, overriding defaults, colorizing output, and elevating privilege), and Scripts (for transforming data and chaining subcommands). We conjecture that identifying common customization practices can point to particular usability issues within command-line programs, and that a deeper understanding of these practices can support researchers and tool developers in designing better user experiences. In addition to our analysis, we provide an extensive reproducibility package in the form of a curated dataset together with well-documented computational notebooks enabling further knowledge discovery and a basis for learning approaches to improve command-line workflows.