scientific software development

Jeff AllenQuantitative Biomedical Research Center

UT Southwestern Medical Center BSCI5096 - 3.26.2013

Avoiding Big Mistakes in Scientific ComputingOr: How to Write Code That Doesn’t Jeopardize

Your Professional Reputation or Patient’s Lives

Motivation

• Anil Potti scandal at Duke– Genomic signature identified that would

identify the best chemo based on a patient’s genes.

– Over 100 patients enrolled in clinical trials.– Later discovered gross mishandling of data

and invalidating bugs in software– Alleged manipulation of data– Watch: Lecture from Keith Baggerly

Outline

• Revision Control• Reproducibility and Replicability• Ensuring Code Quality• Resources

Outline

• Revision Control– Introduction & Concepts– Git & GitHub

• Reproducibility and Replicability• Ensuring Code Quality• Resources

Revision Control

• Tracks changes to files over time• Keeps a complete log of all changes ever

made to any file in a project• Supports more collaboration on projects

– Provides an authoritative repository for the code

– Gracefully catch and handle conflicts in files• Various forms in use today including

Mercurial, Git, Subversion

Git

• Modern distributed revision control system– “Distributed” means you have the entire

history of the project on your local machine.– Don’t have to be online to develop.

• Makes improvements in performance and usability on past systems.

• Open-Source and free

GitHub

• A website that hosts Git repositories.• You can “push” your own Git repositories

to their site to gain:– A web interface – easier way to view your

files and track changes– Control who has access to which projects– Project organization – hosts documentation,

bug-tracking, etc.– Social platform – the “Facebook” of coding– Client-Side graphical user interface

GITHUB DEMONSTRATION

GitHub Client - GUI

• Only works with GitHub.• Much easier to use and navigate.• Mac and Windows versions.• On campus: Need to open Git Shell and

run:git config --global http.proxy http://proxy.swmed.edu:3128

GitHub Client

GITHUB CLIENT DEMO

Use Cases

• “This function used to work.”– Look at the changes made to that file since

it last worked.• “Please send me the code used in this

publication.”– Revert the project back to any point in its

history• “I found a bug and fixed it.”

– (Optionally) Allow others to contribute to your projects.

Outline

• Revision Control• Reproducibility and Replicability

– Replicability– Reproducibility

• Ensuring Code Quality• Resources

C. TITUS BROWN http://ivory.idyll.org/blog/replication-i.html

“‘Replicable’ means ‘other people get exactly the same results when doing exactly the same thing’, while ‘reproducible’ means ‘something similar happens in other people's hands.’ The latter is far stronger, in general, because it indicates that your results are not merely some quirk of your setup and may actually be right.”

Replicability

• In order for analysis to be replicable, another researcher must have access to:– The exact same code you used– The exact same data you used

• Any changes (including bug-fixes and other corrections) in your code or data from what you provide will make your results irreplicable. – Must track in a revision control system

Reproducibility

• Requires much more time and effort• Independently arrive at the same

conclusions– Potentially using the same data– Using different techniques and parameters

• May take as much time to reproduce results as it did to produce them the first time

• Should be done in high-stakes (i.e. clinical) applications

Recommended Practices

a. Use a revision control system such as GitHub

b. To ensure replicability, clone your repository on another computer and re-run all your analysis. Ensure you get the same results.• This is a good test of replicability.• Knowing you’ll have to do this will make

you write better organized code.

c. If it’s really important, ask a colleague to reproduce.

Outline

• Revision Control• Reproducibility and Replicability• Ensuring Code Quality

– Automated Testing– Code reviews

• Resources

Automated Testing

• Unit testing– Very specific target– May have multiple

tests per function• Many unit testing

frameworks– In R: testthat, and

Runit

install.packages(“testthat”)

library(testthat)

Testing Example - Square

Code

square <- function(x){ sq <- 0 for (i in 1:x){ sq <- sq + x } return(sq)}

Testing Example - Square

Code

square <- function(x){ sq <- 0 for (i in 1:x){ sq <- sq + x } return(sq)}

Tests

expect_that( square(3), equals(9)) #Passes

Testing Example - Square

Code

square <- function(x){ sq <- 0 for (i in 1:x){ sq <- sq + x } return(sq)}

Tests

expect_that(square(3), equals(9)) #Passesexpect_that(square(5), equals(25)) #Passes

Test-Driven Development (TDD)

• If you see a bug:1. Write a test that fails2. Fix the bug3. Show that the test now passes4. Commit to revision control

Testing Example - Square

Code

square <- function(x){ sq <- 0 for (i in 1:x){ sq <- sq + x } return(sq)}

Tests

expect_that(square(3), equals(9)) #Passesexpect_that(square(5), equals(25)) #Passes

Testing Example - Square

Code

square <- function(x){ sq <- 0 for (i in 1:x){ sq <- sq + x } return(sq)}

Tests

expect_that(square(3), equals(9)) #Passesexpect_that(square(5), equals(25)) #Passesexpect_that(square(2.5), equals(6.25)) #Fails

Testing Example - Square

Code

square <- function(x){ sq <- 0 for (i in 1:x){ sq <- sq + x } return(sq)}

Tests

expect_that(square(3), equals(9)) #Passesexpect_that(square(5), equals(25)) #Passesexpect_that(square(2.5), equals(6.25)) #Failsexpect_that(square(-2), equals(4)) #Fails

Test-Driven Development (TDD)

• If you see a bug:1. Write a test that fails2. Fix the bug3. Show that the test now passes4. Commit to revision control

Testing Example - Square

Code

square <- function(x){ sq <- x * x return(sq)}

Test-Driven Development (TDD)

• If you see a bug:1. Write a test that fails2. Fix the bug3. Show that the test now passes4. Commit to revision control

Testing Example - Square

Code

square <- function(x){ sq <- x * x return(sq)}

Testing Example - Square

Code

square <- function(x){ sq <- x * x return(sq)}

Tests

expect_that(square(3), equals(9)) #Passesexpect_that(square(5), equals(25)) #Passesexpect_that(square(2.5), equals(6.25)) #Passesexpect_that(square(-2), equals(4)) #Passes

Test-Driven Development (TDD)

• If you see a bug:1. Write a test that fails2. Fix the bug3. Show that the test now passes4. Commit to revision control

Test-Driven Development (TDD)

• Advantages– Ensure that problematic areas are well-

tested– Regression testing – ensure old bugs don’t

ever come back– Confidently approach old code– More assured in handling someone else’s

code– Saves you time over manual testing

Code Reviews

• Get more than one set of eyes on your code

• Lightweight– Email to get quick feedback– GitHub is great for this

• Formal– Have a meeting to audit– Less than 500 LOC per meeting

Extreme – Pair Programming• Two programmers share a single workstation

• Both participate, though only one can type

• Significant learning opportunities for both

• Can strategically pair:–Senior with Junior, mentoring–Statistician with Developer, mutual

learning• Improvements in code quality

compensate for short-term efficiency loss– fewer bugs, easier code to maintain

Testing Example - Square

Code

square <- function(x){ sq <- x * x return(sq)}

Tests

expect_that(square(3), equals(9)) #Passesexpect_that(square(5), equals(25)) #Passesexpect_that(square(2.5), equals(6.25)) #Passesexpect_that(square(-2), equals(4)) #Passes

Testing Example - Square

Code

square <- function(x){ x^2}

Tests

expect_that(square(3), equals(9)) #Passesexpect_that(square(5), equals(25)) #Passesexpect_that(square(2.5), equals(6.25)) #Passesexpect_that(square(-2), equals(4)) #Passes

Outline

• Revision Control• Reproducibility and Replicability• Ensuring Code Quality• Resources

Resources

• Software Carpentry– www.software-carpentry.org – Volunteer organization focused on teaching

these topics to scientific audiences– Contact us (

Jeffrey.Allen@UTSouthwestern.edu) if you’d be interested in attending a local Boot Camp

• GitHub Documentation– https://help.github.com/ – Great documentation on how to use Git

and/or GitHub

Resources

• Unit Testing in R– http://cran.r-project.org/web/packages/RUnit

/index.html– http://cran.r-project.org/web/packages/testt

hat/index.html– http://journal.r-project.org/archive/2011-1/RJ

ournal_2011-1_Wickham.pdf

Suggested Next Steps

• Watch Lecture from Keith Baggerly• Register for a GitHub account (free),

explore• Write an R function and cover it with unit

tests using the test_that framework• Then check into a public GitHub repo