As we build software projects, complexity and technical debt are bound to creep into our code. To counteract these tendencies it is necessary to calculate and track metrics that highlight areas of improvement so that they can be acted on. To aid in identifying areas of your application that are breeding grounds for incidental complexity Anthony Shaw created Wily. In this episode he explains how Wily traverses the history of your repository and computes code complexity metrics over time and how you can use that information to guide your refactoring efforts.
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- Your host as usual is Tobias Macey and today I’m interviewing Anthony Shaw about Wily, a command-line application for tracking and reporting on complexity of Python tests and applications
- How did you get introduced to Python?
- Can you start by describing what Wily is and what motivated you to create it?
- What is software complexity and why should developers care about it?
- What are some methods for measuring complexity?
- I know that Python has the McCabe tool, but what other methods are there for determining complexity, both in Python and for other languages?
- What kinds of useful signals can you derive from evaluating historical trends of complexity in a codebase?
- What are some other useful metrics for tracking and maintaining the health of a software project?
- Once you have established the points of complexity in your software, what are some strategies for remediating it?
- What are your favorite tools for refactoring?
- What are some of the aspects of developer-oriented tools that you have found to be most important in your own projects?
- What are your plans for the future of Wily, or any other tools that you have in mind to aid in producing healthy software?
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