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Why Do Computational Scientists Trust Their So
- 1. Why do climate modellers trust their software? Jon Pipitone Advisor: Steve Easterbrook University of Toronto @Uvic, June 2009
- 4. What am I missing?
- 5. "If we knew what we were doing, it wouldn't be called research, would it?" - attributed to Albert Einstein
- 6. What is climate modelling? A kind of computational science
- 9. Program outputs are the results of the experiment.
- 10. Scientific software development my focus
- 12. What is climate modelling? Source: Easterbrook, CUSEC'09 (Source: IPCC AR4, 2007)
- 13. What is climate modelling? Source: Easterbrook, CUSEC'09 (Source: IPCC AR4, 2007)
- 14. General Circulation Models Source: Easterbrook, CUSEC'09 © Crown Copyright
- 19. Model-intercomparisons Source: Easterbrook, CUSEC'09
- 21. Validation Notes Source: Easterbrook, CUSEC'09 © Crown Copyright
- 22. Core problems with V&V
- 23. Validation notes Bit reproducibility Core problems with V&V
- 25. What does this mean for software quality?
- 26. (note, we're not talking about model quality!)
- 27. How do we judge quality in scientific software?
- 33. Model crashes during a run
- 34. Model runs, but variables drift out of tolerance
- 36. The right results for the “wrong reasons” (e.g. over-tuning)
- 37. Expected improvement not achieved Source: Easterbrook, CUSEC'09
- 38. Measuring Quality: Defect Density So, Is climate modelling software really that good?
- 40. When are we counting: pre- or post-release?
- 42. No standards in the literature
- 46. What makes a piece of software bad ?
- 47. How do you know when you're done?
- 48. How do you train newcomers?
- 49. or...
- 50. When have you had to delay releasing due to a bug? Why?
- 51. Tell me the story behind these and other bugs...
- 54. Already have connections with CM groups
- 58. What do climate modellers do when coding to guarantee correctness?
- 59. What are their notions of quality wrt to code?
- 60. How can we benchmark computational scientists' code quality?
- 63. Breakout by defect types (but, what are they?)
- 64. Maybe: static fault density using automated tool
- 65. Examine several climate models (>3?)
- 67. Use questions given previously to guide conversation
- 68. Investigate the story of a defect, judgement calls that were made.
- 69. ~5 defect stories per climate modelling centre
- 74. … objective the study?