{"title":"Pynblint：提高 Python Jupyter 笔记本质量的质量保证工具","authors":"Luigi Quaranta, Fabio Calefato, Filippo Lanubile","doi":"10.1016/j.softx.2024.101959","DOIUrl":null,"url":null,"abstract":"<div><div>Jupyter Notebook is widely recognized as a crucial tool for data science professionals and students. Its interactive and self-documenting nature makes it particularly suitable for data-driven programming tasks. Nonetheless, it faces criticism for its limited support for software engineering best practices and its tendency to encourage bad programming habits, such as non-linear code execution. These issues often result in non-reproducible, poorly documented, and low-quality notebook code. In this paper, we introduce <span>Pynblint</span>, a static analyzer for Python Jupyter notebooks. <span>Pynblint</span> is designed to help data scientists write better notebooks, easy to understand and reproduce. We report on how we validated <span>Pynblint</span> with both professional data scientists and students, receiving overall positive feedback. Additionally, we discuss the potential of <span>Pynblint</span> to facilitate research inquiries into computational notebooks.</div></div>","PeriodicalId":21905,"journal":{"name":"SoftwareX","volume":"28 ","pages":"Article 101959"},"PeriodicalIF":2.4000,"publicationDate":"2024-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Pynblint: A quality assurance tool to improve the quality of Python Jupyter notebooks\",\"authors\":\"Luigi Quaranta, Fabio Calefato, Filippo Lanubile\",\"doi\":\"10.1016/j.softx.2024.101959\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>Jupyter Notebook is widely recognized as a crucial tool for data science professionals and students. Its interactive and self-documenting nature makes it particularly suitable for data-driven programming tasks. Nonetheless, it faces criticism for its limited support for software engineering best practices and its tendency to encourage bad programming habits, such as non-linear code execution. These issues often result in non-reproducible, poorly documented, and low-quality notebook code. In this paper, we introduce <span>Pynblint</span>, a static analyzer for Python Jupyter notebooks. <span>Pynblint</span> is designed to help data scientists write better notebooks, easy to understand and reproduce. We report on how we validated <span>Pynblint</span> with both professional data scientists and students, receiving overall positive feedback. Additionally, we discuss the potential of <span>Pynblint</span> to facilitate research inquiries into computational notebooks.</div></div>\",\"PeriodicalId\":21905,\"journal\":{\"name\":\"SoftwareX\",\"volume\":\"28 \",\"pages\":\"Article 101959\"},\"PeriodicalIF\":2.4000,\"publicationDate\":\"2024-11-12\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"SoftwareX\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S2352711024003297\",\"RegionNum\":4,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"COMPUTER SCIENCE, SOFTWARE ENGINEERING\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"SoftwareX","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2352711024003297","RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"COMPUTER SCIENCE, SOFTWARE ENGINEERING","Score":null,"Total":0}
Pynblint: A quality assurance tool to improve the quality of Python Jupyter notebooks
Jupyter Notebook is widely recognized as a crucial tool for data science professionals and students. Its interactive and self-documenting nature makes it particularly suitable for data-driven programming tasks. Nonetheless, it faces criticism for its limited support for software engineering best practices and its tendency to encourage bad programming habits, such as non-linear code execution. These issues often result in non-reproducible, poorly documented, and low-quality notebook code. In this paper, we introduce Pynblint, a static analyzer for Python Jupyter notebooks. Pynblint is designed to help data scientists write better notebooks, easy to understand and reproduce. We report on how we validated Pynblint with both professional data scientists and students, receiving overall positive feedback. Additionally, we discuss the potential of Pynblint to facilitate research inquiries into computational notebooks.
期刊介绍：
SoftwareX aims to acknowledge the impact of software on today''s research practice, and on new scientific discoveries in almost all research domains. SoftwareX also aims to stress the importance of the software developers who are, in part, responsible for this impact. To this end, SoftwareX aims to support publication of research software in such a way that: The software is given a stamp of scientific relevance, and provided with a peer-reviewed recognition of scientific impact; The software developers are given the credits they deserve; The software is citable, allowing traditional metrics of scientific excellence to apply; The academic career paths of software developers are supported rather than hindered; The software is publicly available for inspection, validation, and re-use. Above all, SoftwareX aims to inform researchers about software applications, tools and libraries with a (proven) potential to impact the process of scientific discovery in various domains. The journal is multidisciplinary and accepts submissions from within and across subject domains such as those represented within the broad thematic areas below: Mathematical and Physical Sciences; Environmental Sciences; Medical and Biological Sciences; Humanities, Arts and Social Sciences. Originating from these broad thematic areas, the journal also welcomes submissions of software that works in cross cutting thematic areas, such as citizen science, cybersecurity, digital economy, energy, global resource stewardship, health and wellbeing, etcetera. SoftwareX specifically aims to accept submissions representing domain-independent software that may impact more than one research domain.
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