Chapter 18. Testing, Debugging, and Optimizing

You're not finished with a programming task when you're done writing the code; you're finished when the code runs correctly and with acceptable performance. Testing (covered in "Testing" on page 452) means verifying that code runs correctly by exercising the code under known conditions and checking that results are as expected. Debugging (covered in "Debugging" on page 461) means discovering causes of incorrect behavior and repairing them (repair is often easy, once you figure out the causes).

Optimizing (covered in "Optimization" on page 474) is often used as an umbrella term for activities meant to ensure acceptable performance. Optimizing breaks down into benchmarking (measuring performance for given tasks to check that it's within acceptable bounds), profiling (instrumenting the program to identify 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).

This chapter covers the three subjects in the natural order in which they occur in development: testing first and foremost, debugging next, and optimizing last. However, most programmers' enthusiasm focuses on optimization: testing and debugging are often (wrongly, in my opinion) perceived as being chores, while optimization is perceived as being fun. As a consequence, if you were to read only one section of the chapter, I would suggest that section be "Developing a Fast-Enough Python Application" on page 474, which summarizes the Pythonic approach to optimizationclose to Jackson's classic "Rules of Optimization: Rule 1: Don't do it. Rule 2 (for experts only): Don't do it yet."

All of these tasks are large and important, and each could fill at least a book by itself. This chapter does not even come close to exploring every related technique and implication; it focuses on Python-specific techniques, approaches, and tools.