Exploring Code Comprehension in Scientific Programming: Preliminary Insights from Research Scientists
This program is tentative and subject to change.
Scientific software—defined as computer programs, scripts, or code used in scientific research, data analysis, modeling, or simulation—has become central to modern research. However, there is limited research on the readability and understandability of scientific code, both of which are vital for effective collaboration and reproducibility in scientific research. This study surveys 57 research scientists from various disciplines to explore their programming backgrounds, practices, and the challenges they face regarding code readability. Our findings reveal that most participants learn programming through self-study or on-the-job training, with 57.9% lacking formal instruction in writing readable code. Scientists mainly use Python and R, relying on comments and documentation for readability. While most consider code readability essential for scientific reproducibility, they often face issues with inadequate documentation and poor naming conventions, with challenges including cryptic names and inconsistent conventions. Our findings also show low adoption of code quality tools and a trend towards utilizing large language models to improve code quality. These findings offer practical insights into enhancing coding practices and supporting sustainable development in scientific software.
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 | ||
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 | ||