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Integrating Pytest Results with GitHub

When joining a new engineering team, one of the first things I do is familiarize myself with the dev and test processes. Especially the tools used to enforce them. In the past 5 years or so, I’ve noticed that a lot of organizations still use older tools that haven’t yet evolved to support modern practices. Even teams that purely develop software can find themselves working around cumbersome systems that hinder instead of enable.

What do I mean by that? Very few of these tools include useful interfaces to leverage integrations with other systems (like REST APIs). Most have no concept of modern dev practices like continuous integration or containerization. Almost all of them want to record pass / fail at a step by step basis as if you’re executing manually. The vast majority are built around a separation between test and dev (some even emphasize it). And a lot of them require the organization to hire “specialists” for the purpose of “customizing” the tool to the team. In my opinion, these types of systems coerce the organization to emphasize blame over quality and team boundaries over productivity.

I’ve been very successful at building long-lived alternatives to these systems in several organizations. I’ve done it enough to know which features are worth including, and which to leave to the test / dev engineers, especially after the advent of continuous integration and delivery.

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Practicality Beats Purity - Intro and Test Pass Rates Topic

A few hours later, I find myself sitting in the “comforts” of my cubicle. The discussion replaying over and over in my head: “An interface with this behavior will integrate with most common language libraries, with no special client code”, I said. The response was: “But then it’s not a design, and the company already decided that’s the route we’re taking.”

I’ve spent many years of my career involved in buzzword dogma discussions. It’s present at all levels of software development, from basic principles, to scheduling, to implementation, its interfaces, its tests, the execution, the infrastructure that runs it and its release mechanisms. Most of the time, people lose track of why or what they are building in favor of claiming they are using some common buzzword, regardless of the effects on architecture, ease of use, customer experience or maintenance costs. My experience shows they don’t even know why the buzzword technology does things a certain way or why it someone chose it in the first place. Factual or data-based counterargument results in an almost “religious” discussion and even shaming.

Given today’s ease of communication and the ability to share our experiences, it’s great that we try to educate other folks on the problems we typically face throughout our lives and careers. Especially the specific principles used in managing their solutions.

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Engineering the Tests that Matter

Going beyond the checkbox

As I sit in my standard issued cubicle, going through a typical test results report from the typical external test shop, I begin to shake my head in disgust.

I’m looking at a spreadsheet with endless rows and columns that associate a thumbs up / thumbs down with each one-liner that’s supposed to describe the test that executed.

What does all of this mean? Does that mean the test completed? Aborted? What does a thumbs up mean if a high priority issue was logged against it? Wait! If I read through this issue, the comments trail indicates that the test itself — not the item being tested — was modified to get a passing result! What is all this? Does any of it actually imply any type of quality level in the product that I’m testing?

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