Optimizing Python Software through Clean Code: Practices and Principles

Abstract

While the principles of Clean Code and SOLID have long been recognized in software development circles, their consistent application remains a challenge for many projects. A significant percentage of software projects – both completed and ongoing – struggle to adhere to these principles at some degree. This issue is particularly relevant within the context of Python development, given the language’s remarkable growth in popularity over the past two decades.

The present thesis aims to address these challenges by offering a comprehensive set of guide-lines, best practices, tools, and ethical considerations obtained from an exhaustive literature review of Clean Code principles, meticulously tailored to accommodate the particularities of Python.

By navigating through the fundamentals and delving into concrete implementations of clean code principles as applied to the Python programming language, this journey aims to offer readers more than just techniques. It attempts to infuse a mindset of efficiency and cleanliness in their approach to software development. Through practical examples and real-world applications, this thesis not only equips readers with the tools and methodologies for writing cleaner code but also builds a broader framework and work ethics and mentality to maintain and en-hance productivity.

Furthermore, a case study centered on a code refactoring exercise is explored to provide readers with a deeper understanding of how the principles of clean code can be applied in specific context in Python. This case study serves as an opportunity for readers to put into practice the knowledge and framework mentality acquired throughout the theorical framework presented in this document. By analysing real-world examples and discussing behind various refactoring decisions, valuable insights can be gain into the practical application of clean code principles in their own development projects.