testing: More restructuring for the Nose source

[swc-testing-nose.git] / testing / nose / README.md

diff --git a/testing/nose/README.md b/testing/nose/README.md
index b1e949aff4f9889eb212046e1495021c3405ae26..49596d9434da0780172e7a8981d0ad4d68012748 100644 (file)
--- a/testing/nose/README.md
+++ b/testing/nose/README.md
@@ -1,52 +1,10 @@
-# Testing
+# Nose: A Python Testing Framework
 
-![image](media/test-in-production.jpg)
+The testing framework we'll discuss today is called [nose][]. However,
+there are several other testing frameworks available in most
+language. Most notably there is [JUnit][] in Java which arguably
+invented the testing framework.
 
-# What is testing?
 
-Software testing is a process by which one or more expected behaviors
-and results from a piece of software are exercised and confirmed. Well
-chosen tests will confirm expected code behavior for the extreme
-boundaries of the input domains, output ranges, parametric combinations,
-and other behavioral **edge cases**.
-
-# Why test software?
-
-Unless you write flawless, bug-free, perfectly accurate, fully precise,
-and predictable code **every time**, you must test your code in order to
-trust it enough to answer in the affirmative to at least a few of the
-following questions:
-
--   Does your code work?
--   **Always?**
--   Does it do what you think it does? ([Patriot Missile Failure][patriot])
--   Does it continue to work after changes are made?
--   Does it continue to work after system configurations or libraries
-    are upgraded?
--   Does it respond properly for a full range of input parameters?
--   What's the limit on that input parameter?
--   What about **edge or corner cases**?
--   How will it affect your [publications][]?
-
-## Verification
-
-*Verification* is the process of asking, "Have we built the software
-correctly?" That is, is the code bug free, precise, accurate, and
-repeatable?
-
-## Validation
-
-*Validation* is the process of asking, "Have we built the right
-software?" That is, is the code designed in such a way as to produce the
-answers we are interested in, data we want, etc.
-
-## Uncertainty Quantification
-
-*Uncertainty quantification* is the process of asking, "Given that our
-algorithm may not be deterministic, was our execution within acceptable
-error bounds?" This is particularly important for anything which uses
-random numbers, eg Monte Carlo methods.
-
-
-[patriot]: http://www.ima.umn.edu/~arnold/disasters/patriot.html
-[publications]: http://www.nature.com/news/2010/101013/full/467775a.html
+[nose]: https://nose.readthedocs.org/en/latest/
+[JUnit]: http://www.junit.org/