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The road from  good software engineering
to good science
...is a two way street...
Good
 
Quality
Science
Develop theories or models  that let us make predictions about the world
Our world is language...
Do our work so that others can reproduce our results
Science
Reproduce Results
Good Software Engineering
...are those methods that result in software that anyone can use, anytime, anywhere...
...to reproduce our results...
Experimental Results  in NLP/CL Papers
Results are output from test cases
Empirical / experimental results  that you publish are the test cases for your ideas
...and your software...
Can't discount role of softwawe
...although many try...
“It's really the ideas that count...”
“Well, the algorithm  is described in the paper...”
“It's really just a prototype...”
“Well, I got a new computer and I don't think the software made it to the new one...”
“Ummm....my student left and I don't quite know how he did all this...”
I did this experiment on X
Here are the results...
Accept them
No, the software isn't available
Neither is the data
I simply assume you have 8 months available to reinvent my method
And that you can do that from an incomplete description
Cheers!
That's many things
It's not science
Empiricism is Not a Matter of Faith Computational Linguistics September 2008
Software and NLP
 
 
Good Software
It should work
Anytime
Anywhere
For Anyone
...and it should certainly work for you 6 months in the future
...or 5 years from now...
...and it should work for someone else now, and 5 years from now..
...even after you've moved on and aren't answering email, even after the project is over
If your software can do that,  it's pretty well engineered
Will your software work in 40 years?
You should hope so
Make choices that  make that at least possible
Think of your software  as a time capsule
Think of it as your chance for immortality
 
How many hours have you spent away from loved ones, friends, adventure, nature, romance,  and life...to create software?
At least make it last...
Let someone 100 years from now unpack your code and data, and be able to read it, understand it, run it, and modify it
Maybe it's your grandchildren, wondering why they never saw you?
Let yourself be able to do the same thing in 10 years
Will the Linux Kernel  be available and running in X years?
There's a good chance
Company won't go out of business
ANSI C will be around a long time
Virtualization will keep architectures alive even when hardware is gone
Make choices that give your code (and your legacy)  a chance too
Don't rely on the newest  priceiest weirdest goofball proprietary bleeding edge  hardware and software
 
 
Anyone
...with $200?
...with $20,000
...with a PhD in Computer Science?
...and a staff of 10?
...with 4 weeks available to debug?
...and another 6 months  to reimplement?
Good
Software won't stop children from dying of war and disease...
So be a little humble
Appreciate your good fortune
And push yourself a little harder
Don't act all whiny and put-upon when people ask you for  code or data
Especially since a lot of that work is funded by people working very hard making less in a month than we make a in a week
...or that we spend going to NAACL
Appreciate our good fortune
Good Science
Produces theories that make reliable predictions about the world
Reproducible Experimental Results
Anytime
Anywhere
For Anyone
Gravity
A Good Theory
Works now
Will work in 10 years
Works here
Works on the moon
Works for me
Works for you
Falling Objects
Gravity is a force,  not an artifact
Telescope
Works anytime,  anywhere,  for anyone
The old ones still work
 
We share the big ones...
 
If we have access to the same resources, we can reproduce each other's results
If we have access to the same resources, we can reproduce each other's results
We need to work a lot harder (and engineer systems a lot better) to make that happen
Not convinced?
Conduct the following experiment
Randomly select  1 of your papers
Reproduce your results
If you can't...
Do you think anyone else can?
What if nobody could have reproduced Galileo's falling objects experimental results?  Would we simply believe him?
They barely believed him at the time
If you can,  try it for someone else's papers
Lessons from Science
We don't get it right the first time
If I have seen further it is only by standing on the shoulders of giants
 
(who were mostly wrong)
Aristotle (384 – 322 BC)
There are 4 elements
The heavens are different
Different rules apply
Before the telescope, the heavens really were different
Other planets were balls of fire, like the stars, like the sun
 
Ptolemy (90 – 168)
 
 
Crazy?
Very reliably predicts  the movement of heavenly bodies
Instrumentalist
A theory that reliably explains and predicts the existing data
Realistic
A theory that describes things as they “really” are
 
 
A great instrumentalist A great data preserver and collector
Copernicus (1473 - 1543)
 
Wasn't much of an observer
 
Found Ptolmey's model  overly complicated
Wanted a simpler explanation
Wanted a simpler explanation
Came up with another model that was consistent with Ptolmey's data
 
Great!
(Well, better)
Uniform Motion
Perfect circles
 
 
Brahe  (1546 - 1601)
 
A great observational astronomer, the last naked eye astronomer
 
Galileo  (1564 - 1642)
 
 
1609 Telescope
1610  Observed 4 moons of Jupiter
Back to Tycho
Made remarkably accurate observations for 20 years
Knew about Copernicus
Arrived at his own theory
 
A hybrid model
Fits and predicts the observed data
Data Sharing
 
Kepler (1571 - 1630)
 
Why are there 6 planets?
Why are they so positioned?
Geometry and Perfect Solids
 
In 1601 Tycho  bequeathed his data...
Kepler's Laws of Planetary Motion
Varying velocity
Elliptical Orbits
...around the Sun
It was left to Newton to work out what held the planets in place and made them move...
History of Science?
We are wrong many many times before we are right
Progress happens  when people leave their  data and instruments behind
Ptolemy  (90 - 168) Copernicus  (1473 - 1543) Tycho (1546 – 1601) Galileo (1564 - 1642) Kepler  (1571 - 1630) Newton (1642 - 1727)
Good science and good software  assume you don't get it right at first
Leave something behind for your successors to build on
Here lies our fried Ted We're sad that's he dead http://www.d.umn.edu/~tpederse

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The road from good software engineering to good science...is a two way street