Best Practices for Scientific Computing
Posted by Cheng Soon Ong on November 28, 2012
I've been following the progress of Software Carpentry for some years now, and have been very impressed by their message that software is the new telescope, and we should invest time and effort to build up skills to ensure that our software is the best quality possible. Otherwise, how can we be sure that our new discoveries are not due to some instrument error?
They wrote a nice short paper titled "Best Practices for Scientific Computing" that highlights practices that would improve the quality of the software, and hence improve research productivity. Here are the 10 recommendations (along with the sub-recommendations).
1. Write programs for people, not computers.
1.1 a program should not require its readers to hold more than a handful of facts in memory at once
1.2 names should be consistent, distinctive, and meaningful
1.3 code style and formatting should be consistent
1.4 all aspects of software development should be broken down into tasks roughly an hour long
2. Automate repetitive tasks.
2.1 rely on the computer to repeat tasks
2.2 save recent commands in a file for re-use
2.3 use a build tool to automate their scientific workflows
3. Use the computer to record history.
3.1 software tools should be used to track computational work automatically
4. Make incremental changes.
4.1 work in small steps with frequent feedback and course correction
5. Use version control.
5.1 use a version control system
5.2 everything that has been created manually should be put in version control
6. Don’t repeat yourself (or others).
6.1 every piece of data must have a single authoritative representation in the system
6.2 code should be modularized rather than copied and pasted
6.3 re-use code instead of rewriting it
7. Plan for mistakes.
7.1 add assertions to programs to check their operation
7.2 use an off-the-shelf unit testing library
7.3 turn bugs into test cases
7.4 use a symbolic debugger
8. Optimize software only after it works correctly.
8.1 use a profiler to identify bottlenecks
8.2 write code in the highest-level language possible
9. Document the design and purpose of code rather than its mechanics.
9.1 document interfaces and reasons, not implementations
9.2 refactor code instead of explaining how it works
9.3 embed the documentation for a piece of software in that software
10. Conduct code reviews.
10.1 use code review and pair programming when bringing someone new up to speed and when tackling particularly tricky design, coding, and debugging problems
10.2 use an issue tracking tool
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