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Test & Code :  Python Testing for Software Engineering
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Test & Code : Python Testing for Software Engineering

Author: Brian Okken

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Test & Code is a weekly podcast hosted by Brian Okken.
The show covers a wide array of topics including software engineering, development, testing, Python programming, and many related topics.
When we get into the implementation specifics, that's usually Python, such as Python packaging, tox, pytest, and unittest. However, well over half of the topics are language agnostic, such as data science, DevOps, TDD, public speaking, mentoring, feature testing, NoSQL databases, end to end testing, automation, continuous integration, development methods, Selenium, the testing pyramid, and DevOps.
124 Episodes
pip is the package installer for Python. Often, when you run pip, especially the first time in a new virtual environment, you will see something like: WARNING: You are using pip version 20.1.1; however, version 20.2 is available. You should consider upgrading via the 'python -m pip install --upgrade pip' command. And you should. Because 20.2 has a new dependency resolver. Get in the habit, until October, of replacing pip install with pip install --use-feature=2020-resolver. This flag is new in the 20.2 release. This new pip dependency resolver is the result of a lot of work. Five of the people involved with this work are joining the show today: Bernard Tyers, Nicole Harris, Paul Moore, Pradyun Gedam, and Tzu-ping Chung. We talk about: * pip dependency resolver changes * user experience research and testing * crafting good error messages * efforts to improve the test suite * testing pip with pytest * some of the difficulties with testing pip * working with a team on a large project * working with a large code base * bringing new developers into a large project Special Guests: Bernard Tyers, Nicole Harris, Paul Moore, Pradyun Gedam, and Tzu-ping Chung.
Lots of Python projects are starting to use GitHub Actions for Continous Integration & Deployment (CI/CD), as well as other workflows. Tania Allard, a Senior Cloud Developer Advocate at Microsoft, joins the show to answer some of my questions regarding setting up a Python project to use Actions. Some of the topics covered: How to get started with GitHub Actions for a Python project? What are workflow files? Does it matter what the file name is called? Can I have / Should I have more than one workflow? Special Guest: Tania Allard.
A great resume is key to landing a great software job. There's no surprise there. But so many people make mistakes on their resume that can very easily be fixed. Randall Kanna is on the show today to help us understand how to improve our resumes, and in turn, help us have better careers. Special Guest: Randall Kanna.
Len Wanger works on industrial 3D printers. And I was pleased to find out that there's a bunch of Python in those printers as well. In this episode we talk about: 3D printers What are the different types of 3D printers? Where are 3D printed industrial parts being used? Why use one type of additive manufacturing over another? Python in 3D printing hardware. What are Finite State Machines, FSMs? Benefits of FSMs for testing, logging, and breaking a complex behavior into small testable parts. Benefits of simulation in writing and testing software to control hardware. Special Guest: Len Wanger.
FastAPI is a modern, fast (high-performance), web framework for building APIs with Python based on standard Python type hints. Typer is a library for building CLI applications, also based on Python type hints. Type hints and many other details are intended to make it easier to develop, test, and debug applications using FastAPI and Typer. The person behind FastAPI and Typer is Sebastián Ramírez. Sebastián is on the show today, and we discuss: FastAPI Rest APIs Swagger UI Future features of FastAPI Starlette Typer Click Testing with Typer and Click Typer autocompletion Typer CLI Special Guest: Sebastián Ramírez.
There's stuff going on in Python packaging and pyproject.toml. Brett and I talk about some upcoming work on Python packaging, such as: editable installs the need for standardization configuration of other tools in pyproject.toml And then get off on tangents and talk about: why it's good to have packages like pip, toml, setuptools, wheel, etc not part of the standard library should we remove some stuff from the standard library the standard library using unittest for testing the standard library why not hypothesis I didn't bring up "why not pytest?" but you know I was thinking it. why CPython and not C++Python and more Special Guest: Brett Cannon.
Code Coverage or Test Coverage is a way to measure what lines of code and branches in your code that are utilized during testing. Coverage tools are an important part of software engineering. But there's also lots of different opinions about using it. - Should you try for 100% coverage? - What code can and should you exclude? - What about targets? I've been asked many times what I think about code coverage or test coverage. This episode is a train of thought brain dump on what I think about code coverage. We'll talk about: - how I use code coverage to help me write source code - line coverage and branch coverage - behavior coverage - using tests to ask and answer questions about the system under test - how to target coverage just to the code you care about - excluding code - good reasons and bad reasons to exclude code And also the Pareto Principle or 80/20 rule, and the law of diminishing returns and how that applies (or doesn't) to test coverage.
The Python extension for VS Code is most downloaded extension for VS Code. Brett Cannon is the manager for the distributed development team of the Python extension for VS Code. In this episode, Brett and I discuss the Python extension and VS Code, including: pytest support virtual environment support how settings work, including user and workspace settings multi root projects testing Python in VS Code debugging and pydevd jump to cursor feature upcoming features Special Guest: Brett Cannon.
pytest plugins are an amazing way to supercharge your test suites, leveraging great solutions from people solving test problems all over the world. In this episode Michael and I discuss 15 favorite plugins that you should know about. We also discuss fixtures and plugins and other testing tools that work great with pytest * tox * GitHub Actions * * Selenium + splinter with pytest-splinter * Hypothesis And then our list of pytest plugins: 1. pytest-sugar 1. pytest-cov 1. pytest-stress 1. pytest-repeat 1. pytest-instafail 1. pytest-metadata 1. pytest-randomly 1. pytest-xdist 1. pytest-flake8 1. pytest-timeout 1. pytest-spec 1. pytest-picked 1. pytest-freezegun 1. pytest-check 1. fluentcheck That last one isn't a plugin, but we also talked about pytest-splinter at the beginning. So I think it still counts as 15. Special Guest: Michael Kennedy.
One of the great things about attending in person coding conferences, such as PyCon, is the hallway track, where you can catch up with people you haven't seen for possibly a year, or maybe even the first time you've met in person. Nina is starting something like the hallway track, online, on twitch, and it's already going, so check out the first episode of Python Tea ( Interesting coincidence is that this episode is kind of like a hallway track discussion between Nina and Brian. We've had Nina on the show a couple times before, but it's been a while. In 2018, we talked about Mentoring on episode 44 ( In 2019, we talked about giving Memorable Tech Talks in episode 71 ( In this episode, we catch up with Nina, find out what she's doing, and talk about a bunch of stuff, including: Live Coding Online Conferences Microsoft Python team Python Tea, an online hallway track Q&A with Python for VS Code team Python on hardware Adafruit Device Simulator Express CircuitPython Tricking out your command prompt Zsh and Oh My Zsh Emacs vs vi key bindings for shells Working from home Special Guest: Nina Zakharenko.
"The mission of the Python Software Foundation is to promote, protect, and advance the Python programming language, and to support and facilitate the growth of a diverse and international community of Python programmers." That's a lot of responsibility, and to that end, the PSF Board Directors help out quite a bit. If you want to be a part of the board, you can. There's an election coming up right around the corner and you gotta get your nomination in by May 31. You can also join the PSF if you want to vote for who gets to be part of the board. But what does it really mean to be on the Board, and what are some of the things the PSF does? To help answer those questions, I've got Ewa Jodlowska, the PSF Executive Director, and Christopher Neugebauer, a current board member, on the show today. I've also got some great links in the show notes if we don't answer your questions and you want to find out more. Special Guests: Christopher Neugebauer and Ewa Jodlowska.
Technical debt has to be dealt with on a regular basis to have a healthy product and development team. The impacts of technical debt include emotional drain on engineers and slowing down development and can adversely affect your hiring ability and retention. But really, what is technical debt? Can we measure it? How do we reduce it, and when? James Smith, the CEO of Bugsnag, joins the show to talk about technical debt and all of these questions. Special Guest: James Smith.
"Code is read much more often than it is written." - Guido van Rossum This is true for both production code and test code. When you are trying to understand why a test is failing, you'll be very grateful to the test author if they've taken the care to make it readable. David Seddon came up with 6 principles to help us write more readable tests. We discuss these, as well as more benefits of readable tests. David's 6 Principles of Readable Tests: Profit from the work of others Put naming to work Show only what matters Don’t repeat yourself Arrange, act, assert Aim high Special Guest: David Seddon.
In both unittest and pytest, when a test function hits a failing assert, the test stops and is marked as a failed test. What if you want to keep going, and check more things? There are a few ways. One of them is subtests. Python's unittest introduced subtests in Python 3.4. pytest introduced support for subtests with changes in pytest 4.4 and a plugin, called pytest-subtests. Subtests are still not really used that much. But really, what are they? When could you use them? And more importantly, what should you watch out for if you decide to use them? That's what Paul Ganssle and I will be talking about today. Special Guest: Paul Ganssle.
Django supports testing out of the box with some cool extensions to unittest. However, many people are using pytest for their Django testing, mostly using the pytest-django plugin. Adam Parkin, who is known online as CodependentCodr (, joins us to talk about migrating an existing Django project from unittest to pytest. Adam tells us just how easy this is. Special Guest: Adam Parkin.
Financial services have their own unique testing development challenges. But they also have lots of the same challenges as many other software projects. Eric Bergemann joins Brian Okken to discuss: Specific testing challenges in the financial services domain CI/CD : Continuous Integration, Continuous Deployment TDD : Test Driven Development Confidence from testable applications Testing strategies to add coverage to legacy systems Testing the data and test cases themselves DevOps Continuous testing Manual testing procedures BDD & Gherkin Hiring in vs training industry knowledge Special Guest: Eric Bergemann.
Apache Spark is a unified analytics engine for large-scale data processing. PySpark blends the powerful Spark big data processing engine with the Python programming language to provide a data analysis platform that can scale up for nearly any task. Johnathan Rioux, author of "PySpark in Action", joins the show and gives us a great introduction of Spark and PySpark to help us decide how to get started and decide whether or not to decide if Spark and PySpark are right you. Special Guest: Jonathan Rioux.
Hypothesis is the Python tool used for property based testing. Hypothesis claims to combine "human understanding of your problem domain with machine intelligence to improve the quality of your testing process while spending less time writing tests." In this episode Alexander Hultnér introduces us to property based testing in Python with Hypothesis. Some topics covered: What is property based testing Thinking differently for property based testing Using hypothesis / property based testing in conjunction with normal testing Failures saved and re-run What parts of development/testing is best suited for hypothesis / property based testing Comparing function implementations Testing against REST APIs that use Open API / Swagger with schemathesis Changing the number of tests in different test environments System, integration, end to end, and unit tests Special Guest: Alexander Hultnér.
IDEs can help people with automated testing. In this episode, Paul Everitt and Brian discuss ways IDEs can encourage testing and make it easier for everyone, including beginners. We discuss features that exist and are great, as well as what is missing. The conversation also includes topics around being welcoming to new contributors for both open source and professional projects. We talk about a lot of topics, and it's a lot of fun. But it's also important. Because IDEs can make testing Some topics discussed: Making testing more accessible Test First vs teaching testing last TDD workflow Autorun Rerunning last failures Different ways to run different levels of tests Command line flags and how to access them in IDEs pytest.ini zooming in and out of test levels running parametrizations running tests with coverage and profiling parametrize vs parameterize parametrization identifiers pytest fixture support global configurations / configuration templates coverage and testing and being inviting to new contributors confidence in changes and confidence in contributions navigating code, tests, fixtures grouping tests in modules, classes, directories BDD, behavior driven development, cucumber, pytest-bdd web development testing parallel testing with xdist and IDE support refactor rename Special Guest: Paul Everitt.
The Test Anything Protocol, or TAP, is a way to record test results in a language agnostic way, predates XML by about 10 years, and is still alive and kicking. Matt Layman has contributed to Python in many ways, including his educational newsletter, and his Django podcast, Django Riffs. Matt is also the maintainer of and pytest-tap, two tools that bring the Test Anything Protocol to Python. In this episode, Matt and I discuss TAP, it's history, his involvement, and some cool use cases for it. Special Guest: Matt Layman.
Comments (6)

Max Ong Zong Bao

I would be interested to invite you as keynote for PyCon Singapore in June and I would love to know more on your PyTest online courses when it releases.

Jan 3rd

Александр Михеев

Such a great episode! I've even listened to it twice

Dec 9th

Eduardo Costa

I enjoyed this episode. Hope more episodes on this subject.

Mar 15th

Leora Juster

react tables

Jan 12th


another great episode

Dec 16th

Antonio Andrade

Thanks for sharing these good tips

Dec 9th
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