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

Metrics – tools for static analysis


Static code analysis tools can provide a rich summary of information on the static properties of your code, which can provide insights into aspects like complexity and modifiability/readability of the code.

Python has a lot of third-party tool support, which helps in measuring the static aspects of Python code such as these:

  • Conformance to coding standards like PEP-8

  • Code complexity metrics like the McCabe metric

  • Errors in code such as syntax errors, indentation issues, missing imports, variable overwrites, and others

  • Logic issues in code

  • Code smells

The following are some of the most popular tools in the Python ecosystem which can perform such static analysis:

  • Pylint: Pylint is a static checker for Python code, which can detect a range of coding errors, code smells, and style errors. Pylint uses a style close to PEP-8. The newer versions of Pylint also provide statistics about code complexity, and can print reports. Pylint requires the code to be executed before...