Book Image

Software Architecture with Python

By : Anand Balachandran Pillai
Book Image

Software Architecture with Python

By: Anand Balachandran Pillai

Overview of this book

This book starts by explaining how Python fits into an application's architecture. As you move along, you will get to grips with architecturally significant demands and how to determine them. Later, you’ll gain a complete understanding of the different architectural quality requirements for building a product that satisfies business needs, such as maintainability/reusability, testability, scalability, performance, usability, and security. You will also use various techniques such as incorporating DevOps, continuous integration, and more to make your application robust. You will discover when and when not to use object orientation in your applications, and design scalable applications. The focus is on building the business logic based on the business process documentation, and understanding which frameworks to use and when to use them. The book also covers some important patterns that should be taken into account while solving design problems, as well as those in relatively new domains such as the Cloud. By the end of this book, you will have understood the ins and outs of Python so that you can make critical design decisions that not just live up to but also surpassyour clients’ expectations.
Table of Contents (18 chapters)
Software Architecture with Python
Credits
About the Author
About the Reviewer
www.PacktPub.com
Customer Feedback
Preface
Index

Chapter 10. Techniques for Debugging

Debugging a program can often be as hard, or sometimes, even more difficult than writing it. Quite often, programmers seem to spend an awful amount of time hunting for that elusive bug, the reason for which may be staring them in the face, yet not revealing itself.

Many developers, even the good ones, find troubleshooting a difficult art. Most often, programmers resort to complicated debugging techniques when simple approaches such as properly placed print statements and strategically commented code would do the trick.

Python comes with its own set of problems when it comes to debugging code. Being a dynamically typed language, type-related exceptions, which happen due to the programmer assuming a type to be something (when it's something else), are pretty common in Python. Name errors and attribute errors fall in a similar category too.

In this chapter, we will exclusively focus on this lesser discussed aspect of software.

Here is a topic wise listing of...