This presentation explores code comprehension challenges in scientific programming based on a survey of 57 research scientists. It reveals that 57.9% of scientists have no formal training in writing readable code. Key findings highlight a "documentation paradox" where documentation is both the most common readability practice and the biggest challenge scientists face. The study identifies critical issues with naming conventions and code organization, noting that 100% of scientists agree readable code is essential for reproducible research. The research concludes with four key recommendations: expanding programming education for scientists, conducting targeted research on scientific code quality, developing specialized tools, and establishing clearer documentation guidelines for scientific software. Presented at: The 33rd International Conference on Program Comprehension (ICPC '25) Date of Conference: April 2025 Conference Location: Ottawa, Ontario, Canada Preprint: https://arxiv.org/abs/2501.10037

1. Exploring Code Comprehension
in Scientific Programming:
Preliminary Insights from Research Scientists
Alyssia Chen, Carol Wong, Bonita Sharif, Anthony Peruma
The 33rd IEEE/ACM International Conference on Program Comprehension
April 27 - 28, 2025 | Ottawa, Ontario, Canada

2. Scientific Software Is Vital to Modern Research
30-35% of scientists' time spent coding
[1,2]
Domain experts first, programmers second
Unique environments create new challenges
Limited research on scientific code comprehension
[1] J. E. Hannay, C. MacLeod, J. Singer, H. P. Langtangen, D. Pfahl and G. Wilson, "How do scientists develop and use scientific software?," 2009 ICSE Workshop on Software Engineering for Computational Science and Engineering, 2009
[2] Prakash Prabhu, Thomas B. Jablin, Arun Raman, Yun Zhang, Jialu Huang, Hanjun Kim, Nick P. Johnson, Feng Liu, Soumyadeep Ghosh, Stephen Beard, Taewook Oh, Matthew Zoufaly, David Walker, and David I. August. 2011. A survey of the practice of computational science. In State of the Practice

3. Study Objective
Their coding backgrounds
Code readability practices
Key challenges in understanding code
Online survey of 57 research
scientists in various disciplines

4. The Scientific Programmer Profile
57.9% have no formal training in
writing readable/maintainable code
Most are self-taught or
learned “on the job”
Python (27.9%), R (26.1%)
Traditional IDEs (43.8%)

5. Documentation and comments are the
most common strategies
49.1% never use code quality tools
Increasing reliance on AI tools like ChatGPT
Readability: Practices & Challenges
Code Comprehension
Barriers
Insufficient comments
No documentation
Poor naming conventions
Messy structure
Hardcoded values
Code Comprehension
Practices
100% of scientists agree:
Readable code is essential for reproducible research

6. 100% of participants agreed that
good naming is key to readability
56% of participants sometimes face
misunderstandings due to naming
Naming Matters
Identifier Naming
Challenges
Names too short or cryptic
Inconsistent styles
Doesn’t reflect its purpose
Generic names
Names that are too similar

7. Targeted
Research
Expand
Programming
Education
Documentation
Paradox
Tool
Support
Takeaways & Implications

8. Thanks!
Alyssia Chen, Carol Wong, Bonita Sharif, Anthony Peruma
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