A Systematic Literature Review of Software Engineering Research on Jupyter Notebook

A Systematic Literature Review of Software Engineering Research on Jupyter Notebook Md Saeed Siddika, Hao Lib, Cor-Paul Bezemera aUniversity of Alberta, Edmonton, Canada bQueen’s University, Kingston, Canada Appendix Table 1: List of primary studies with software engineering (SE) topics and subtopics Sl Title SE Topic Subtopic Ref. 1 Understanding the Characteristics of Visual Contents in Open Source Issue Discussions: A Case Study of Jupyter Notebook. Visualization in notebooks Empirical studies on visualizations [2] 2 JupySim: Jupyter Notebook Similarity Search System. Code reuse and provenance Reusing code snippets by code search [49] 3 Nalin: learning from Runtime Behavior to Find Name-Value Inconsistencies in Jupyter Notebooks. Testing of notebooks Detecting bugs [109] 4 A Static Analysis Framework for Data Science Notebooks. Testing of notebooks Detecting data leakage [153] 5 Assessing the Quality of Computational Notebooks for a Frictionless Transition from Exploration to Production. Best practices Best practices for collaborative use [118] 6 A Large-Scale Comparison of Python Code in Jupyter Notebooks and Scripts. Best practices Following code style standards [42] 7 A Framework to capture and reproduce the Absolute State of Jupyter Notebooks. Managing computational environment and workflow Managing dependencies [180] 8 Runtime provenance refinement for notebooks. Code reuse and provenance Provenance [23] 9 Documentation Matters: HumanCentered AI System to Assist Data Science Code Documentation in Computational Notebooks. Documentation of notebooks Documentation generation [170] 10 Similarity Search on Computational Notebooks. Code reuse and provenance Reusing code snippets by code search [50] 11 Eliciting Best Practices for Collaboration with Computational Notebooks. Best practices Best practices for collaborative use [120] 12 StickyLand: Breaking the Linear Presentation of Computational Notebooks. Readability of notebooks Non-linear visualization [178] 13 Error Identification Strategies for Python Jupyter Notebooks. Testing of notebooks Detecting bugs [131] 14 Bridging between Data Science and Performance Analysis: Tracing of Jupyter Notebooks. Managing computational environment and workflow Performance analysis [186] 15 Automated cell header generator for Jupyter notebooks. Documentation of notebooks Cell header generation [162] 16 VizSmith: Automated Visualization Synthesis by Mining Data-Science Notebooks. Code reuse and provenance Reusing code snippets by code search [6] 17 Restoring the Executability of Jupyter Notebooks by Automatic Upgrade of Deprecated APIs. Managing computational environment and workflow Managing dependencies [197] 18 Splitting, Renaming, Removing: A Study of Common Cleaning Activities in Jupyter Notebooks. Readability of notebooks Refactoring [25] Continued on next page 2 Sl Title SE Topic Subtopic Ref. 19 ToonNote: Improving Communication in Computational Notebooks Using Interactive Data Comics. Readability of notebooks Non-linear visualization [62] 20 NBSearch: Semantic Search and Visual Exploration of Computational Notebooks. Code reuse and provenance Reusing code snippets by code search [81] 21 Fork It: Supporting Stateful Alternatives in Computational Notebooks. Managing computational environment and workflow Computational environment in notebooks [184] 22 What Makes a Well-Documented Notebook? A Case Study of Data Scientists’ Documentation Practices in Kaggle. Documentation of notebooks Documentation generation [169] 23 Automating State Management in Computational Notebooks. Cell execution order [91] 24 HAConvGNN: Hierarchical Attention Based Convolutional Graph Neural Network for Code Documentation Generation in Jupyter Notebooks. Documentation of notebooks Documentation generation [89] 25 Context-aware Execution Migration Tool for Data Science Jupyter Notebooks on Hybrid Clouds. Managing computational environment and workflow Computational environment in notebooks [21] 26 A qualitative study of cleaning in Jupyter notebooks. Readability of notebooks Refactoring [24] 27 Automated Assessment of Quality of Jupyter Notebooks Using Artificial Intelligence and Big Code. Cell execution order [106] 28 Restoring Execution Environments of Jupyter Notebooks. Managing computational environment and workflow Managing dependencies [175] 29 Graph-Augmented Code Summarization in Computational Notebooks. Documentation of notebooks Documentation generation [167] 30 Notebook Archaeology: Inferring Provenance from Computational Notebooks. Code reuse and provenance Provenance [70] 31 ReproduceMeGit: A Visualization Tool for Analyzing Reproducibility of Jupyter Notebooks. Code reuse and provenance Reproducibility [137] 32 How Data Scientists Improve Generated Code Documentation in Jupyter Notebooks. Documentation of notebooks Documentation generation [98] 33 Towards Tracking Provenance from Machine Learning Notebooks. Code reuse and provenance Provenance [66] 34 KGTorrent: A Dataset of Python Jupyter Notebooks from Kaggle. Datasets of notebooks [119] 35 Jupyter Notebooks on GitHub: Characteristics and Code Clones. Code reuse and provenance Code cloning [61] 36 ReSplit: Improving the Structure of Jupyter Notebooks by Re-Splitting Their Cells. Readability of notebooks Refactoring [157] 37 Understanding and improving the quality and reproducibility of Jupyter notebooks. Code reuse and provenance Reproducibility [113] 38 Assessing and Restoring Reproducibility of Jupyter Notebooks. Code reuse and provenance Reproducibility [172] 39 What’s Wrong with Computational Notebooks? Pain Points, Needs, and Design Opportunities. Managing computational environment and workflow Computational environment in notebooks [17] Continued on next page 3 Sl Title SE Topic Subtopic Ref. 40 Your notebook is not crumby enough, REPLace it. Testing of notebooks Detecting bugs [7] 41 Restoring reproducibility of Jupyter notebooks. Code reuse and provenance Reproducibility [173] 42 Better code, better sharing: on the need of analyzing jupyter notebooks. Best practices Following code style standards [174] 43 Notes on notebooks: is Jupyter the bringer of jollity? Cell execution order [149] 44 B2: Bridging Code and Interactive Visualization in Computational Notebooks. Visualization in notebooks Interactive visualization [190] 45 Casual Notebooks and Rigid Scripts: Understanding Data Science Programming. Managing computational environment and workflow Empirical studies on workflows [154] 46 Code Duplication and Reuse in Jupyter Notebooks. Code reuse and provenance Code cloning [69] 47 The Design Space of Computational Notebooks: An Analysis of 60 Systems in Academia and Industry. Managing computational environment and workflow Empirical studies on workflows [74] 48 Testing with Jupyter notebooks: NoteBook VALidation (nbval) plug-in for pytest. Testing of notebooks Detecting bugs [31] 49 Securing Your Collaborative Jupyter Notebooks in the Cloud using Container and Load Balancing Services. Managing computational environment and workflow Computational environment in notebooks [90] 50 Managing Messes in Computational Notebooks. Readability of notebooks Refactoring [48] 51 PySnippet: Accelerating Exploratory Data Analysis in Jupyter Notebook through Facilitated Access to Example Code. Code reuse and provenance Reusing code snippets by code search [181] 52 A large-scale study about quality and reproducibility of jupyter notebooks. Code reuse and provenance Reproducibility [112] 53 PolyJuS: a Squeak/Smalltalk-based polyglot notebook system for the GraalVM. Supporting other programming paradigms [103] 54 Towards Understanding Data Analysis Workflows using a Large Notebook Corpus. Managing computational environment and workflow Empirical studies on workflows [129] 55 Reproducible Research is more than Publishing Research Artefacts: A Systematic Analysis of Jupyter Notebooks from Research Articles. Code reuse and provenance Reproducibility [143] 56 Notes on the Code Quality Culture on Jupyter (Notebooks). Best practices Following code style standards [151] 57 Exploration and Explanation in Computational Notebooks. Documentation of notebooks Empirical studies on documentation [134] 58 ProvBook: Provenance-based Semantic Enrichment of Interactive Notebooks for Reproducibility. Code reuse and provenance Provenance [136] 59 Wrattler: Reproducible, live and polyglot notebooks. Supporting other programming paradigms [111] 60 Interactions for Untangling Messy History in a Computational Notebook. Code reuse and provenance Provenance [63] 61 Ten Simple Rules for Reproducible Research in Jupyter Notebooks. Code reuse and provenance Reproducibility [132] Continued on next page 4 Sl Title SE Topic Subtopic Ref. 62 Aiding Collaborative Reuse of Computational Notebooks with Annotated Cell Folding. Readability of notebooks Non-linear visualization [133] 63 Design and Use of Computational Notebooks. Documentation of notebooks Empirical studies on documentation [135] 64 Dataflow Notebooks: Encoding and Tracking Dependencies of Cells. Managing computational environment and workflow Empirical studies on workflows [71] 65 Collecting and Analyzing Provenance on Interactive Notebooks: When IPython Meets noWorkflow. Code reuse and provenance Provenance [114] 66 Elevating Jupyter Notebook Maintenance Tooling by Identifying and Extracting Notebook Structures. Cell execution order [58] 67 Keeping your Jupyter notebook code quality bar high (and production ready) with Ploomber. Cell execution order [94] 68 Suppose You Had Blocks within a Notebook. Supporting other programming paradigms [165] 69 Pynblint: a static analyzer for Python Jupyter notebooks. Best practices Following code style standards [121] 70 Code Code Evolution: Understanding How People Change Data Science Notebooks Over Time. Documentation of notebooks Empirical studies on documentation [124] 71 davos: a Python "smuggler"; for constructing lightweight reproducible notebooks. Managing computational environment and workflow Managing dependencies [32] 72 Natural Language to Code Generation in Interactive Data Science Notebooks. AI-based coding assistance for notebooks [194] 73 Data Science Through the Looking Glass: Analysis of Millions of GitHub Notebooks and ML.NET Pipelines. Managing computational environment and workflow Empirical studies on workflows [117] 74 Computational reproducibility of Jupyter notebooks from biomedical publications. Code reuse and provenance Reproducibility [138] 75 Provenance-enhanced Root Cause Analysis for Jupyter Notebooks. Testing of notebooks Detecting bugs [191] 76 Bolt-on, Compact, and Rapid Program Slicing for Notebooks [Scalable Data Science]. Readability of notebooks Refactoring [146] 77 Reusing My Own Code: Preliminary Results for Competitive Coding in Jupyter Notebooks. Code reuse and provenance Code cloning [130] 78 Why Visualize Data When Coding? Preliminary Categories for Coding in Jupyter Notebooks. Visualization in notebooks Empirical studies on visualizations [145] 79 Reproducible Notebook Containers using Application Virtualization. Code reuse and provenance Reproducibility [3] 80 Comparing Execution Traces of Jupyter Notebook for Checking Correctness of Refactoring. Readability of notebooks Refactoring [139] 81 Data Leakage in Notebooks: Static Detection and Better Processes. Testing of notebooks Detecting data leakage [192] Continued on next page 5 Sl Title SE Topic Subtopic Ref. 82 Cell2Doc: ML Pipeline for Generating Documentation in Computational Notebooks. Documentation of notebooks Documentation generation [96] 83 On the Design of AI-powered Code Assistants for Notebooks. AI-based coding assistance for notebooks [92] 84 Refactoring in Computational Notebooks. Readability of notebooks Refactoring [88] 85 Static Analysis of Data Transformations in Jupyter Notebooks. Testing of notebooks Detecting data leakage [101] 86 Taming the Diversity of Computational Notebooks. AI-based coding assistance for notebooks [8] 87 Weedle: Composable Dashboard for DataCentric NLP in Computational Notebooks. Visualization in notebooks Interactive visualization [73] 88 Visualising data science workflows to support third-party notebook comprehension: an empirical study. Visualization in notebooks Empirical studies on visualizations [126] 89 WhatsNext: Guidance-enriched Exploratory Data Analysis with Interactive, Low-Code Notebooks. AI-based coding assistance for notebooks [18] 90 Jup2Kub: algorithms and a system to translate a Jupyter Notebook pipeline to a fault tolerant distributed Kubernetes deployment. Managing computational environment and workflow Computational environment in notebooks [26] 91 An approach to assess the quality of Jupyter projects published by GLAM institutions Best practices Following code style standards [12] 92 Measuring How Data Science Notebooks Evolve Over Time Managing computational environment and workflow Empirical studies on workflows [123] 93 Do Code Quality and Style Issues Differ Across (Non-) Machine Learning Notebooks? Yes! Best practices Following code style standards [148] 94 ElasticNotebook: Enabling Live Migration for Computational Notebooks Managing computational environment and workflow Computational environment in notebooks [85] 95 EDAssistant: Supporting Exploratory Data Analysis in Computational Notebooks with In Situ Code Search and Recommendation. Code reuse and provenance Reusing code snippets by code search [82] 96 Enhancing Comprehension and Navigation in Jupyter Notebooks with Static Analysis. Documentation of notebooks Cell header generation [164] 97 Facilitating Dependency Exploration in Computational Notebooks. Cell execution order [9] 98 Mining the Characteristics of Jupyter Notebooks in Data Science Projects. Managing computational environment and workflow Empirical studies on workflows [20] 99 MLProvLab: Provenance Management for Data Science Notebooks. Code reuse and provenance Provenance [65] 100 Notable: On-the-fly Assistant for Data Storytelling in Computational Notebooks. Documentation of notebooks Documentation generation [76] 101 On Code Reuse from StackOverflow: An Exploratory Study on Jupyter Notebook. Code reuse and provenance Code cloning [193] Continued on next page 6 Sl Title SE Topic Subtopic Ref. 102 Typhon: Automatic Recommendation of Relevant Code Cells in Jupyter Notebooks. Code reuse and provenance Reusing code snippets by code search [125] 103 Improving Quantum Developer Experience with Kubernetes and Jupyter Notebooks. Managing computational environment and workflow Computational environment in notebooks [68] 104 JUmPER: Performance Data Monitoring, Instrumentation and Visualization for Jupyter Notebooks. Managing computational environment and workflow Performance analysis [187] 105 Unlocking Insights: Semantic Search in Jupyter Notebooks. Code reuse and provenance Reusing code snippets by code search [77] 106 A Flexible Cell Classification for ML Projects in Jupyter Notebooks. Documentation of notebooks Cell header generation [110] 107 Persist: Persistent and Reusable Interactions in Computational Notebooks. Code reuse and provenance Provenance [33] 108 Static analysis driven enhancements for comprehension in machine learning notebooks. Documentation of notebooks Cell header generation [163] 109 InkSight: Leveraging Sketch Interaction for Documenting Chart Findings in Computational Notebooks. Documentation of notebooks Documentation generation [86] 110 Beyond Syntax: Unleashing the Power of Computational Notebooks Code Metrics in Documentation Generation. Documentation of notebooks Documentation generation [37] 111 JupyterLab in Retrograde: Contextual Notifications That Highlight Fairness and Bias Issues for Data Scientists. Managing computational environment and workflow Computational environment in notebooks [47] 112 OutlineSpark: Igniting AI-powered Presentation Slides Creation from Computational Notebooks through Outlines. Documentation of notebooks Documentation generation [171] 113 Unveiling Data Preprocessing Patterns in Computational Notebooks. Managing computational environment and workflow Empirical studies on workflows [38] 114 Can We Do Better with What We Have Done? Unveiling the Potential of ML Pipeline in Notebooks. Managing computational environment and workflow Empirical studies on workflows [199] 115 Hidden Gems in the Rough: Computational Notebooks as an Uncharted Oasis for IDEs. Managing computational environment and workflow Computational environment in notebooks [158] 116 Demonstration of ElasticNotebook: Migrating Live Computational Notebook States. Managing computational environment and workflow Computational environment in notebooks [83] 117 NotePlayer: Engaging Computational Notebooks for Dynamic Presentation of Analytical Processes. Documentation of notebooks Documentation generation [108] 118 An Automated Evaluation Approach for Jupyter Notebook Code Cell Recommender Systems. Code reuse and provenance Reusing code snippets by code search [4] 119 Facilitating Mixed-Methods Analysis with Computational Notebooks. Managing computational environment and workflow Computational environment in notebooks [198] 120 Predicting the Understandability of Computational Notebooks through Code Metrics Analysis. Datasets of notebooks [35] Continued on next page 7 Sl Title SE Topic Subtopic Ref. 121 Kishu: Time-Traveling for Computational Notebooks. Managing computational environment and workflow Computational environment in notebooks [84] 122 Histree: A Tree-Based Experiment History Tracking Tool for Jupyter Notebooks. Code reuse and provenance Provenance [152] 123 Make It Make Sense! Understanding and Facilitating Sensemaking in Computational Notebooks. Readability of notebooks Non-linear visualization [16] 124 Understanding and Mitigating the Challenges of Securing Jupyter Notebooks Online. Managing computational environment and workflow Computational environment in notebooks [127] 125 DistilKaggle: A Distilled Dataset of Kaggle Jupyter Notebooks. Datasets of notebooks [36] 126 Jupyter Notebook Attacks Taxonomy: Ransomware, Data Exfiltration, and Security Misconfiguration. Managing computational environment and workflow Computational environment in notebooks [13] 127 Understanding Feedback Mechanisms in Machine Learning Jupyter Notebooks. Testing and debugging Empirical studies on testing and debugging [147] 128 Extending Jupyter with Multi-Paradigm Editors. Supporting other programming paradigms [182] 129 Multiverse Notebook: Shifting Data Scientists to Time Travelers. Cell execution order [140] 130 Design Concerns for Integrated Scripting and Interactive Visualization in Notebook Environments. Visualization in notebooks Interactive visualization [144] 131 Pynblint: A quality assurance tool to improve the quality of Python Jupyter notebooks. Best practices Following code style standards [122] 132 Evaluating Navigation and Comparison Performance of Computational Notebooks on Desktop and in Virtual Reality. Visualization in notebooks Interactive visualization [54] 133 SuperNOVA: Design Strategies and Opportunities for Interactive Visualization in Computational Notebooks. Visualization in notebooks Empirical studies on visualizations [179] 134 Don’t Step on My Toes: Resolving Editing Conflicts in Real-Time Collaboration in Computational Notebooks. Best practices Best practices for collaborative use [168] 135 NotebookGPT - Facilitating and Monitoring Explicit Lightweight Student GPT Help Requests During Programming Exercises. AI-based coding assistance for notebooks [34] 136 Contextualized Data-Wrangling Code Generation in Computational Notebooks. AI-based coding assistance for notebooks [52] 137 Bug Analysis in Jupyter Notebook Projects: An Empirical Study. Testing and Debugging Empirical studies on testing and debugging [22] 138 Using Run-Time Information to Enhance Static Analysis of Machine Learning Code in Notebooks. Testing and debugging Detecting bugs [176] 139 BISCUIT: Scaffolding LLM-Generated Code with Ephemeral UIs in Computational Notebooks. AI-based coding assistance for notebooks [19] Continued on next page 8 Sl Title SE Topic Subtopic Ref. 140 Explainability in JupyterLab and Beyond: Interactive XAI Systems for Integrated and Collaborative Workflows. Best practices Best practices for collaborative use [44] 141 Untangling Knots: Leveraging LLM for Error Resolution in Computational Notebooks. Testing and debugging Detecting bugs [43] 142 From Computational to Conversational Notebooks. AI-based coding assistance for notebooks [183] 143 Charting EDA: Characterizing Interactive Visualization Use in Computational Notebooks with a Mixed-Methods Formalism. Visualization in notebooks Empirical studies on visualizations and interactive visualization [189] 144 Debug Smarter, Not Harder: AI Agents for Error Resolution in Computational Notebooks. Testing and debugging Detecting bugs [39] 145 Why do Machine Learning Notebooks Crash? Testing and debugging Empirical studies on testing and debugging [177] 146 KOGI: A Seamless Integration of ChatGPT into Jupyter Environments for Programming Education. AI-based coding assistance for notebooks [72] 147 A Tool for Detecting Similarities in Jupyter Notebooks Used as Assessment Reports Code reuse and provenance Reusing code snippets by code search [5] 148 Analyzing the reproducibility of researchrelated Jupyter notebooks at scale. Code reuse and provenance Reproducibility [95] 149 Are the Majority of Public Computational Notebooks Pathologically NonExecutable? Code reuse and provenance Reproducibility [102] 150 Characterising Bugs in Jupyter Platform. Testing and debugging Testing and debugging [155] 151 DatawiseAgent: A Notebook-Centric LLM Agent Framework for Automated Data Science AI-based coding assistance for notebooks [195] 152 Enhancing Computational Notebooks with Code+Data Space Versioning Code reuse and provenance Provenance [29] 153 Enhancing Knowledge Preservation in Machine Learning Research: Jupyter Notebooks as an Interactive Documentation Tool. Documentation of notebooks Documentation generation [142] 154 Evolving the Computational Notebook: A Two-Dimensional Canvas for Enhanced Human-AI Interaction Visualization in notebooks Interactive visualization [40] 155 Exploring Organizational Strategies in Immersive Computational Notebooks Cell execution order [55] 156 Exploring the Jupyter Ecosystem: An Empirical Study of Bugs and Vulnerabilities Testing and debugging Empirical studies on testing and debugging [57] 157 How Scientists Use Jupyter Notebooks: Goals, Quality Attributes, and Opportunities Best practices Following code style standards [51] 158 InterLink: Linking Text with Code and Output in Computational Notebooks Documentation of notebooks Documentation generation [87] Continued on next page 9 [31] Fangohr, H., Fauske, V., Kluyver, T., Albert, M., Laslett, O., CortésOrtuño, D., Beg, M., Ragan-Kelly, M., 2020. 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