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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:\n                    <jats:sc>Shortcuts<\/jats:sc>\n                    (for\n                    <jats:italic>nicknaming commands<\/jats:italic>\n                    ,\n                    <jats:italic>abbreviating subcommands<\/jats:italic>\n                    , and\n                    <jats:italic>bookmarking locations<\/jats:italic>\n                    ),\n                    <jats:sc>Modifications<\/jats:sc>\n                    (for\n                    <jats:italic>substituting commands<\/jats:italic>\n                    ,\n                    <jats:italic>overriding defaults<\/jats:italic>\n                    ,\n                    <jats:italic>colorizing output<\/jats:italic>\n                    , and\n                    <jats:italic>elevating privilege<\/jats:italic>\n                    ), and\n                    <jats:sc>Scripts<\/jats:sc>\n                    (for\n                    <jats:italic>transforming data<\/jats:italic>\n                    and\n                    <jats:italic>chaining subcommands<\/jats:italic>\n                    ). 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.\n                  <\/jats:p>","DOI":"10.1007\/s10664-021-10036-y","type":"journal-article","created":{"date-parts":[[2021,12,14]],"date-time":"2021-12-14T00:02:41Z","timestamp":1639440161000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["An empirical investigation of command-line customization"],"prefix":"10.1007","volume":"27","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1496-0531","authenticated-orcid":false,"given":"Michael","family":"Schr\u00f6der","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"J\u00fcrgen","family":"Cito","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2021,12,14]]},"reference":[{"key":"10036_CR1","unstructured":"Agarwal M, Barroso JJ, Chakraborti T, Dow EM, Fadnis KP, Godoy B, Talamadupula K (2020) CLAI: A platform for AI skills on the command line. arXiv:2002.00762 [cs.HC]"},{"key":"10036_CR2","doi-asserted-by":"crossref","unstructured":"Alom MZ, Carminati B, Ferrari E (2019) Helping users managing context-based privacy preferences. 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