Method Names in Jupyter Notebooks: An Exploratory Study
This program is tentative and subject to change.
Method names play an important role in communicating the purpose and behavior of their functionality. Research has shown that high-quality names significantly improve code comprehension and the overall maintainability of software. However, these studies primarily focus on naming practices in traditional software development. There is limited research on naming patterns in Jupyter Notebooks, a popular environment for scientific computing and data analysis. In this exploratory study, we analyze the naming practices found in 691 methods across 384 Jupyter Notebooks, focusing on three key aspects: naming style conventions, grammatical composition, and the use of abbreviations and acronyms. Our findings reveal distinct characteristics of notebook method names, including a preference for conciseness and deviations from traditional naming patterns. We identified 68 unique grammatical patterns, with only 55.57% of methods beginning with a verb. Further analysis revealed that half of the methods with return statements do not start with a verb. We also found that 30.39% of method names contain abbreviations or acronyms, representing mathematical or statistical terms and image processing concepts, among others. We envision our findings contributing to developing specialized tools and techniques for evaluating and recommending high-quality names in scientific code and creating educational resources tailored to the notebook development community.
This program is tentative and subject to change.
Mon 28 AprDisplayed time zone: Eastern Time (US & Canada) change
11:00 - 12:30 | Empirical Findings, Future Visions, Recommendations Replications and Negative Results (RENE) / Early Research Achievements (ERA) / Tool Demonstration / Research Track at 205 | ||
11:00 10mTalk | Terminal Lucidity: Envisioning the Future of the Terminal Research Track | ||
11:10 6mTalk | Exploring Code Comprehension in Scientific Programming: Preliminary Insights from Research Scientists Early Research Achievements (ERA) Alyssia Chen University of Hawaii at Manoa, Carol Wong University of Hawaii at Manoa, Bonita Sharif University of Nebraska-Lincoln, USA, Anthony Peruma University of Hawai‘i at Mānoa | ||
11:16 10mTalk | Method Names in Jupyter Notebooks: An Exploratory Study Research Track Carol Wong University of Hawaii at Manoa, Gunnar Larsen University of Hawaii at Manoa, Rocky Huang University of Hawaii at Manoa, Bonita Sharif University of Nebraska-Lincoln, USA, Anthony Peruma University of Hawai‘i at Mānoa | ||
11:26 6mTalk | SCALAR: A Part-of-speech Tagger for Identifiers Tool Demonstration Christian Newman , Brandon Scholten Kent State University, Sophia Testa Kent State University, Joshua Behler Kent State University, Syreen Banabilah Kent State University, Michael L. Collard The University of Akron, Michael J. Decker Bowling Green State University, Mohamed Wiem Mkaouer University of Michigan - Flint, Marcos Zampieri George mason University, Eman Abdullah AlOmar Stevens Institute of Technology, USA, Reem Alsuhaibani Prince Sultan University, Anthony Peruma University of Hawai‘i at Mānoa, Jonathan I. Maletic Kent State University | ||
11:32 6mTalk | How do Papers Make into Machine Learning Frameworks: A Preliminary Study on TensorFlow Early Research Achievements (ERA) Federica Pepe University of Sannio, Claudia Farkas York University, Maleknaz Nayebi York University, Giulio Antoniol Ecole Polytechnique de Montreal, Massimiliano Di Penta University of Sannio, Italy | ||
11:38 4mTalk | Toward Neurosymbolic Program Comprehension Early Research Achievements (ERA) Alejandro Velasco William & Mary, Aya Garryyeva William and Mary, David Nader Palacio William & Mary, Antonio Mastropaolo William and Mary, USA, Denys Poshyvanyk William & Mary | ||
11:42 10mTalk | Combining Static Analysis Techniques for Program Comprehension Using Slicito Tool Demonstration Pre-print | ||
11:52 6mTalk | Mining Code Change Patterns in Ada Projects Replications and Negative Results (RENE) | ||
11:58 6mTalk | Telling Software Evolution Stories With Sonification Early Research Achievements (ERA) | ||
12:04 10mTalk | Attributed Multiplex Learning for Analogical Third-Party Library Recommendation and Retrieval Research Track Baihui Sang State Key Laboratory for Novel Software Technology, Nanjing University, Liang Wang Nanjing University, Jierui Zhang Nanjing University, Xianping Tao Nanjing University | ||
12:14 6mTalk | LLM2FedLLM - A Tool for Simulating Federated LLMs for Software Engineering Tasks Tool Demonstration Jahnavi Kumar Indian Institute of Technology Tirupati, India, Siddhartha Gandu Indian Institute of Technology Tirupati, Sridhar Chimalakonda Indian Institute of Technology, Tirupati | ||
12:20 10mLive Q&A | Session's Discussion: "Empirical Findings, Future Visions, Recommendations" Research Track | ||