@article{siddik_systematic_2026,
	title = {A systematic literature review of software engineering research on {Jupyter} notebook},
	volume = {235},
	issn = {0164-1212},
	url = {https://www.sciencedirect.com/science/article/pii/S0164121225004273},
	doi = {https://doi.org/10.1016/j.jss.2025.112758},
	abstract = {Context: Jupyter Notebook has emerged as a versatile tool that transforms how researchers, developers, and data scientists conduct and communicate their work. As the adoption of Jupyter notebooks continues to rise, so does the interest from the software engineering research community in improving the software engineering practices for Jupyter notebooks. Objective: The purpose of this study is to analyze trends, gaps, and methodologies used in software engineering research on Jupyter notebooks. Method: We selected 199 relevant publications up to September 2025, following established systematic literature review guidelines. We explored publication trends, categorized them based on software engineering topics, and reported findings based on those topics. Results: The most popular venues for publishing software engineering research on Jupyter notebooks are related to human-computer interaction instead of traditional software engineering venues. Researchers have addressed a wide range of software engineering topics on notebooks, such as code reuse, readability, and execution environment. Although reusability is one of the research topics for Jupyter notebooks, only 82 of the 199 studies can be reused based on their provided URLs. Additionally, most replication packages are not hosted on permanent repositories for long-term availability and adherence to open science principles. Conclusion: Solutions specific to notebooks for software engineering issues, including testing, refactoring, and documentation, are underexplored. Future research opportunities exist in automatic testing frameworks, refactoring clones between notebooks, and generating group documentation for coherent code cells.},
	journal = {Journal of Systems and Software},
	author = {Siddik, Md Saeed and Li, Hao and Bezemer, Cor-Paul},
	year = {2026},
	keywords = {Data analysis, Jupyter notebook, Software engineering},
	pages = {112758},
}

{"_id":"b633s2rZh5c5cff2D","bibbaseid":"siddik-li-bezemer-asystematicliteraturereviewofsoftwareengineeringresearchonjupyternotebook-2026","author_short":["Siddik, M. S.","Li, H.","Bezemer, C."],"bibdata":{"bibtype":"article","type":"article","title":"A systematic literature review of software engineering research on Jupyter notebook","volume":"235","issn":"0164-1212","url":"https://www.sciencedirect.com/science/article/pii/S0164121225004273","doi":"https://doi.org/10.1016/j.jss.2025.112758","abstract":"Context: Jupyter Notebook has emerged as a versatile tool that transforms how researchers, developers, and data scientists conduct and communicate their work. As the adoption of Jupyter notebooks continues to rise, so does the interest from the software engineering research community in improving the software engineering practices for Jupyter notebooks. Objective: The purpose of this study is to analyze trends, gaps, and methodologies used in software engineering research on Jupyter notebooks. Method: We selected 199 relevant publications up to September 2025, following established systematic literature review guidelines. We explored publication trends, categorized them based on software engineering topics, and reported findings based on those topics. Results: The most popular venues for publishing software engineering research on Jupyter notebooks are related to human-computer interaction instead of traditional software engineering venues. Researchers have addressed a wide range of software engineering topics on notebooks, such as code reuse, readability, and execution environment. Although reusability is one of the research topics for Jupyter notebooks, only 82 of the 199 studies can be reused based on their provided URLs. Additionally, most replication packages are not hosted on permanent repositories for long-term availability and adherence to open science principles. Conclusion: Solutions specific to notebooks for software engineering issues, including testing, refactoring, and documentation, are underexplored. Future research opportunities exist in automatic testing frameworks, refactoring clones between notebooks, and generating group documentation for coherent code cells.","journal":"Journal of Systems and Software","author":[{"propositions":[],"lastnames":["Siddik"],"firstnames":["Md","Saeed"],"suffixes":[]},{"propositions":[],"lastnames":["Li"],"firstnames":["Hao"],"suffixes":[]},{"propositions":[],"lastnames":["Bezemer"],"firstnames":["Cor-Paul"],"suffixes":[]}],"year":"2026","keywords":"Data analysis, Jupyter notebook, Software engineering","pages":"112758","bibtex":"@article{siddik_systematic_2026,\n\ttitle = {A systematic literature review of software engineering research on {Jupyter} notebook},\n\tvolume = {235},\n\tissn = {0164-1212},\n\turl = {https://www.sciencedirect.com/science/article/pii/S0164121225004273},\n\tdoi = {https://doi.org/10.1016/j.jss.2025.112758},\n\tabstract = {Context: Jupyter Notebook has emerged as a versatile tool that transforms how researchers, developers, and data scientists conduct and communicate their work. As the adoption of Jupyter notebooks continues to rise, so does the interest from the software engineering research community in improving the software engineering practices for Jupyter notebooks. Objective: The purpose of this study is to analyze trends, gaps, and methodologies used in software engineering research on Jupyter notebooks. Method: We selected 199 relevant publications up to September 2025, following established systematic literature review guidelines. We explored publication trends, categorized them based on software engineering topics, and reported findings based on those topics. Results: The most popular venues for publishing software engineering research on Jupyter notebooks are related to human-computer interaction instead of traditional software engineering venues. Researchers have addressed a wide range of software engineering topics on notebooks, such as code reuse, readability, and execution environment. Although reusability is one of the research topics for Jupyter notebooks, only 82 of the 199 studies can be reused based on their provided URLs. Additionally, most replication packages are not hosted on permanent repositories for long-term availability and adherence to open science principles. Conclusion: Solutions specific to notebooks for software engineering issues, including testing, refactoring, and documentation, are underexplored. Future research opportunities exist in automatic testing frameworks, refactoring clones between notebooks, and generating group documentation for coherent code cells.},\n\tjournal = {Journal of Systems and Software},\n\tauthor = {Siddik, Md Saeed and Li, Hao and Bezemer, Cor-Paul},\n\tyear = {2026},\n\tkeywords = {Data analysis, Jupyter notebook, Software engineering},\n\tpages = {112758},\n}\n\n\n\n","author_short":["Siddik, M. S.","Li, H.","Bezemer, C."],"key":"siddik_systematic_2026","id":"siddik_systematic_2026","bibbaseid":"siddik-li-bezemer-asystematicliteraturereviewofsoftwareengineeringresearchonjupyternotebook-2026","role":"author","urls":{"Paper":"https://www.sciencedirect.com/science/article/pii/S0164121225004273"},"keyword":["Data analysis","Jupyter notebook","Software engineering"],"metadata":{"authorlinks":{}}},"bibtype":"article","biburl":"https://bibbase.org/zotero-group/schulzkx/5158478","dataSources":["JFDnASMkoQCjjGL8E"],"keywords":["data analysis","jupyter notebook","software engineering"],"search_terms":["systematic","literature","review","software","engineering","research","jupyter","notebook","siddik","li","bezemer"],"title":"A systematic literature review of software engineering research on Jupyter notebook","year":2026}