As science becomes more reliant on computational methods, researchers often use programming languages like Python and R to analyze data. However, scientific code is frequently written by graduate students and postdocs with little formal training in software development, leading to a higher likelihood of errors. Debugging, a critical skill for fixing these errors, is not commonly taught to scientists. Experts suggest documenting conditions that cause bugs, using print statements to track a program’s internal state, and employing Python’s logging library for detailed event logs. These methods help ensure that code functions correctly and produces reliable results.
QUESTION: How might improving coding skills among scientists impact the quality and reliability of scientific research in the future?
Got bugs? Here’s how to catch the errors in your scientific software
