Test and Code
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41: Testing in DevOps and Agile - Anthony Shaw
We talk with Anthony Shaw about some of the testing problems facing both DevOps teams, and Agile teams. We also talk about his recent pull request accepted into pytest. Special Guest: Anthony Shaw.
40: On Podcasting - Adam Clark
Adam is the host of The Gently Mad (https://thegentlymad.com/) podcast, and teaches the steps in creating and growing a podcast in his course Irresistible Podcasting (https://irresistiblepodcasting.com). He was one of the people who inspired Brian to get the Test & Code podcast started in the first place. Brian took his course in 2015. Adam is in the process of updating the course, and building a community around it. Warning: This may be an episode to listen to with headphones if you have...
39: Thorough software testing for critical features
Complete and exhaustive testing is not possible. Nor would it be fun, or maintainable, or a good use of your time. However, some functionality is important enough to make sure the test behavior coverage is thorough enough to have high confidence in it's quality. In this episode, we discuss 3 techniques that can be combined to quickly generate test cases. We then talk about how to implement them efficiently in pytest. The techniques covered are: equivalence partitioning boundary value...
38: Prioritize software tests with RCRCRC
RCRCRC was developed by Karen Nicole Johnson. In this episode we discuss the mnemonic/heuristic and use it to prioritize tests for the cards application. Recent: new features, new areas of code Core: essential functions must continue to work, your products USPs (Unique Selling Propositions) Risk: some areas of an application pose more risk, perhaps areas important to customers but not used regularly by the development team. Configuration sensitive: code that’s dependent on environment...
37: What tests to write first
This episode starts down the path of test strategy with the first tests to write in either a legacy system or a project just getting off it's feet. We cover: My approach to testing existing systems. Put names to strategies so we can refer to them later. Explain the strategies in general terms and explain why they are useful. Discuss how these strategies are used in an example project. (The code is available on github). Strategies covered today: Dog Fooding Exploratory Testing Tracer Bullet...
36: Stephanie Hurlburt - Mentoring and Open Office Hours
Stephanie is a co-founder and graphics engineer at Binomial. She works on Basis, an image compressor, and has customers in games, video, mapping, and any application that has lots of image data. Stephanie has also been encouraging experienced engineers to open up their twitter DMs to questions from anyone, to help mentor people not only in technical questions, but in career questions as well. She also sets aside some time to mentor people through skype when written form just doesn't cut...
35: Continuing Education and Certificate Programs at UW
There are lots of ways to up your skills. Of course, I'm a big fan of learning through reading books, such as upping your testing skills by reading Python Testing with pytest. And then there are online learning systems and MOOCs. At the other end of the spectrum is a full blown university degree. One option kind of in the middle is continuing education programs available through some universities, such as University of Washington. To discuss this option with me in more depth, we've got...
34: TDD and Test First
An in depth discussion of Test Driven Development (TDD) should include a discussion of Test First. So that's where we start. Why write tests first? How do you know what tests to write? What are the steps for test first? Isn't this just TDD? Functional Tests vs Unit Tests
33: Katharine Jarmul - Testing in Data Science
A discussion with Katharine Jarmul, aka kjam, about some of the challenges of data science with respect to testing. Some of the topics we discuss: experimentation vs testing testing pipelines and pipeline changes automating data validation property based testing schema validation and detecting schema changes using unit test techniques to test data pipeline stages testing nodes and transitions in DAGs testing expected and unexpected data missing data and non-signals corrupting a dataset...