You’re not finished with a programming task when
you’re done writing the code:
you’re finished when your code is running correctly
and with acceptable performance.
verifying that your code is running correctly by exercising the code
under known conditions and checking that the results are as expected.
Debugging means discovering the causes of
incorrect behavior and removing them (the removal is often easy once
you have figured out the causes).
Optimizing is often used as an umbrella term for
activities meant to ensure acceptable performance. Optimizing breaks
benchmarking (measuring performance
for given tasks and checking that it’s within
profiling (instrumenting the
program to find out what parts are performance bottlenecks), and
optimizing proper (removing bottlenecks to make overall program
performance acceptable). Clearly, you can’t remove
performance bottlenecks until you’ve found out where
they are (using profiling), which in turn requires knowing that there
are performance problems (using benchmarking).
All of these tasks are large and important, and each could fill a book by itself. This chapter does not explore every related technique and implication; it focuses on Python-specific techniques, approaches, and tools.
In this chapter, I distinguish between two rather different kinds of testing: unit testing and system testing. Testing is a rich and important field, and even ...