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14.1 Introduction

Credit: Mark Hammond, co-author of Python Programming on Win32 (O'Reilly)

The first computer I had in my home was a 64-KB Z80 CP/M machine. Having the machine available at home meant I had much more time to deeply explore this exciting toy. Turbo Pascal had just been released, and it seemed the obvious progression from the various BASIC dialects and assemblers I had been using. Even then, I was somehow drawn towards developing reusable libraries for my programs, and as my skills and employment progressed, I remained drawn to building tools that assist developers as much as building end-user applications.

Building tools for developers means that debugging and testing are often in the foreground. Although images of an interactive debugger may pop into your head, the concepts of debugging and testing are much broader than you may initially think. Debugging and testing are sometimes an inseparable cycle. Your testing will often lead you to discover bugs. You debug until you believe you understand the cause of the error and make the necessary changes. Rinse and repeat as required.

Often, debugging and testing are more insidious. I am a big fan of Python's assert statement, and every time I use it, I am debugging and testing my program. Large projects will often develop strategies to build debugging and testing capabilities directly into the application itself, such as centralized logging and error handling. In larger projects, it could be argued that this style of debugging and testing is more critical than the post mortem activities I just described.

Python, in particular, supports a variety of techniques to help developers in their endeavors. The introspective and dynamic nature of Python (the result of Guido's "we are all consenting adults" philosophy of programming) means that your opportunities for debugging techniques are limited only by your imagination. You can replace functions at runtime, add methods to classes, and extract everything about your program that there is to know. All at runtime, and all quite simple and Pythonic.

In this chapter, you will find a nice collection of recipes from which even the most hardened critic will take gastronomic delight. Whether you want customized error logging, deep diagnostic information in Python tracebacks, or even help with your garbage, you have come to the right place. So tuck in your napkin; your next course has arrived!

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