Cover Image

Integrating Code Metrics into Automated Documentation Generation for Computational Notebooks

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Ghahfarokhi, Mojtaba Mostafavi, Jahantigh, Hamed, Asadi, Alireza, Heydarnoori, Abbas
Format:	Preprint
Published:	2026
Subjects:	Software Engineering
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Predicting the Understandability of Computational Notebooks through Code Metrics Analysis
by: Ghahfarokhi, Mojtaba Mostafavi, et al.
Published: (2024)

Automated Generation of Commit Messages in Software Repositories
by: Palakodeti, Varun Kumar, et al.
Published: (2025)

GIRT-Model: Automated Generation of Issue Report Templates
by: Nikeghbal, Nafiseh, et al.
Published: (2024)

Can GitHub Issues Help in App Review Classifications?
by: Abedini, Yasaman, et al.
Published: (2023)

Hybrid LLM Routing for Efficient App Feedback Classification
by: Abedini, Yasaman, et al.
Published: (2025)

LIA: Supervised Fine-Tuning of Large Language Models for Automatic Issue Assignment
by: Khosravani, Arsham, et al.
Published: (2026)

LinkAnchor: An Autonomous LLM-Based Agent for Issue-to-Commit Link Recovery
by: Akhavan, Arshia, et al.
Published: (2025)

Prioritizing App Reviews for Developer Responses on Google Play
by: Jafari, Mohsen, et al.
Published: (2025)

Contextualized Data-Wrangling Code Generation in Computational Notebooks
by: Huang, Junjie, et al.
Published: (2024)

Human to Document, AI to Code: Comparing GenAI for Notebook Competitions
by: Settewong, Tasha, et al.
Published: (2025)

A Regression Testing Framework with Automated Assertion Generation for Machine Learning Notebooks
by: Yao, Yingao Elaine, et al.
Published: (2025)

A Study of Scientific Computational Notebook Quality
by: Kashiwa, Shun, et al.
Published: (2026)

Are the Majority of Public Computational Notebooks Pathologically Non-Executable?
by: Nguyen, Tien, et al.
Published: (2025)

Typhon: Automatic Recommendation of Relevant Code Cells in Jupyter Notebooks
by: Ragkhitwetsagul, Chaiyong, et al.
Published: (2024)

Hidden Gems in the Rough: Computational Notebooks as an Uncharted Oasis for IDEs
by: Titov, Sergey, et al.
Published: (2024)

Enhancing LLM-Based Code Generation with Complexity Metrics: A Feedback-Driven Approach
by: Sepidband, Melika, et al.
Published: (2025)

CodeScore-R: An Automated Robustness Metric for Assessing the FunctionalCorrectness of Code Synthesis
by: Yang, Guang, et al.
Published: (2024)

Similarity-Based Assessment of Computational Reproducibility in Jupyter Notebooks
by: Hossain, A S M Shahadat, et al.
Published: (2025)

Automated Modernization of Machine Learning Engineering Notebooks for Reproducibility
by: Jin, Bihui, et al.
Published: (2026)

Why do Machine Learning Notebooks Crash? An Empirical Study on Public Python Jupyter Notebooks
by: Wang, Yiran, et al.
Published: (2024)

Evolving the Computational Notebook: A Two-Dimensional Canvas for Enhanced Human-AI Interaction
by: Grotov, Konstantin, et al.
Published: (2025)

Containing the Reproducibility Gap: Automated Repository-Level Containerization for Scholarly Jupyter Notebooks
by: Samuel, Sheeba, et al.
Published: (2026)

Generating Maximal Configurations and Their Variants Using Code Metrics
by: Yavuz, Tuba, et al.
Published: (2024)

Automated and Context-Aware Code Documentation Leveraging Advanced LLMs
by: Sarker, Swapnil Sharma, et al.
Published: (2025)

Untangling Knots: Leveraging LLM for Error Resolution in Computational Notebooks
by: Grotov, Konstantin, et al.
Published: (2024)

Method Names in Jupyter Notebooks: An Exploratory Study
by: Wong, Carol, et al.
Published: (2025)

Understanding Feedback Mechanisms in Machine Learning Jupyter Notebooks
by: Shome, Arumoy, et al.
Published: (2024)

Mining the Characteristics of Jupyter Notebooks in Data Science Projects
by: Choetkiertikul, Morakot, et al.
Published: (2023)

Runtime-Augmented LLMs for Crash Detection and Diagnosis in ML Notebooks
by: Wang, Yiran, et al.
Published: (2026)

Static Analysis Driven Enhancements for Comprehension in Machine Learning Notebooks
by: Venkatesh, Ashwin Prasad Shivarpatna, et al.
Published: (2023)

Condor: A Code Discriminator Integrating General Semantics with Code Details
by: Liang, Qingyuan, et al.
Published: (2024)

GENCNIPPET: Automated Generation of Code Snippets for Supporting Programming Questions
by: Mondal, Saikat, et al.
Published: (2025)

Bridging Design and Development with Automated Declarative UI Code Generation
by: Zhou, Ting, et al.
Published: (2024)

GeoJSEval: An Automated Evaluation Framework for Large Language Models on JavaScript-Based Geospatial Computation and Visualization Code Generation
by: Chen, Guanyu, et al.
Published: (2025)

Consistency Meets Verification: Enhancing Test Generation Quality in Large Language Models Without Ground-Truth Solutions
by: Taherkhani, Hamed, et al.
Published: (2026)

Automated Test Generation from Program Documentation Encoded in Code Comments
by: Denaro, Giovanni, et al.
Published: (2025)

An Integrated Platform for LEED Certification Automation Using Computer Vision and LLM-RAG
by: Lee, Jooyeol
Published: (2025)

Beyond Code: Empirical Insights into How Team Dynamics Influence OSS Project Selection
by: Nirmani, Shashiwadana, et al.
Published: (2026)

Observing Fine-Grained Changes in Jupyter Notebooks During Development Time
by: Titov, Sergey, et al.
Published: (2025)

Automated Repair of Ambiguous Problem Descriptions for LLM-Based Code Generation
by: Jia, Haoxiang, et al.
Published: (2025)