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Enhancing Computer Science with Experimental Methodology

Explore experimental methodology in computer science with a focus on observation, hypothesis, experimentation, analysis, and reporting. Understand the crucial role of experiments in validating ideas. Discover practical methods, analysis tools, and the importance of statistical significance. Embrace the scientific approach to enrich your skills as a computer scientist.

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Enhancing Computer Science with Experimental Methodology

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  1. Experiments in Computer Science

  2. Experiments in Computer Science "The fundamental principle of science, the definition almost, is this: the sole test of the validity of any idea is experiment" • Richard P. Feynman • Tried and true experimental scientific methodology from Physics, Biology, Chemistry ... • Often not followed in Computer Science • Let's be better Computer Scientists!

  3. Scientific Methodology • Observe • (Devise solution) • Hypothesize • Design • Experiment • Analyze • Report

  4. Methodology: Observe and Understand • Find Problem • Test: make of Linux kernel • Build: memory intensive programs • Read: Linux Hacker’s guide says … • Understand Relationships • Hard page faults are expensive • Logical memory larger than physical

  5. Methodology: Devise and Hypothesize • Devise Solution (unless empirical) • Claypool Reliable Audio Protocol (CRAP) • Claypool buffering algorithm • Make Hypothesis • Generalization about relationships • Soft page faults are common • Malloc does not cause page fault • Needs to be tested (not proven)

  6. Methodology: Experiment • Design Experiment • Variable: variable workload • Control: baseline workload • Run Experiment “Whoa! That’s not what I expected!” • Bug in code • Back to “Run” • Uncontrolled event (system backup) • Back to “Design” • Insufficient understanding (Unix scheduling) • Back to “Understanding”

  7. Methodology: Analyze • Interpretation and Evaluation • Statistical significance • mean, confidence intervals, correlation, goodness of fit • Does data support or reject hypothesis? • Explanation of other phenomena • Better code reduces page faults, improves performance

  8. Graph: A Data Analysis Tool • A picture is worth a thousand words • Title, label axes (units!), legend

  9. Dirty Little Secrets • Mini-experiments (no, “Pilot Tests”) • Hypotheses after the fact • Running yields understanding • Results here mean results there • Controlled system still says meaningful things about the real world • Observing a system will not change it

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