2020
DOI: 10.1109/tse.2018.2871058
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The Adoption of JavaScript Linters in Practice: A Case Study on ESLint

Abstract: A linter is a static analysis tool that warns software developers about possible code errors or violations to coding standards. By using such a tool, errors can be surfaced early in the development process when they are cheaper to fix. For a linter to be successful, it is important to understand the needs and challenges of developers when using a linter. In this paper, we examine developers' perceptions on JavaScript linters. We study why and how developers use linters along with the challenges they face while… Show more

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Cited by 46 publications
(31 citation statements)
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References 63 publications
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“…The use of static analysis tools is quite common among software development teams (e.g., [15,16]). These tools, however, rely on bug pattern detectors that are manually crafted and fine-tuned by static analysis experts.…”
Section: Related Workmentioning
confidence: 99%
“…The use of static analysis tools is quite common among software development teams (e.g., [15,16]). These tools, however, rely on bug pattern detectors that are manually crafted and fine-tuned by static analysis experts.…”
Section: Related Workmentioning
confidence: 99%
“…The described approach has an advantage compared to the common development of webpages: the ability to reuse these unified web interface elements to conduct different web-surveys. Another important feature of the standard is that it makes it possible to verify the described questionnaire before the actual survey, which is similar to the linting process [21,22].…”
Section: Data Collectionmentioning
confidence: 99%
“…Current solutions: Taking into consideration the domain and the architecture of the system under study has been gaining attention from the community in the last years. Although linters are widely used [35], [36], and quality monitoring strategies such as Continuous Inspection have been proposed [26], researchers have shown that the domain of the application matters when it comes to the presence of code smells [22], that code metric distributions are statistically different among the different architectural roles of classes in a system [3], [23], and that specific architectures may have their own specific smells [2], [19].…”
Section: Contextually Measuring the Quality Of Object-oriented Somentioning
confidence: 99%