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Episode 4 - 7 Practices for High Quality Maintainable Code

13 mins
2020-02-24 engineering testing
  • Code is complicated, hard to test, difficult to understand and can frustrate others.
  • Writing cleaner code can save you from reimplementing software simply that you cannot understand.
  • It’s an iterative process and there’s several principles to help you do that.
  • Keep it Simple Stupid (KISS) tells us to avoid unnecessary complexity and reduce moving parts. The idea is to write for maintainability.
  • Don’t Repeat Yourself (DRY) is about avoiding redundant implementations of the same function. You should think about refactoring.
  • You Aren’t Gonna Need It (YAGNI), an Extreme Programming principle, says we should stick with the requirements and avoid adding unneeded features or functions.
  • Composition over Inheritance asks us to take care when applying classes an inheritance in your design because it can lead to inflexible code.
  • Favoring Readability reminds us that writing software is like writing prose. Organize your code as if you’re writing a novel.
  • Practice Consistency tells us to stick with our decisions throughout the project. Keep the same format, implementation flow and design principles.
  • Consider How to Test a solution before writing it, or at least while writing. It helps you avoid traps that can unnecessarily complicate the code base.
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Uniquely Managing Test Execution Resources using WebSockets

Executing tests for simple applications is complicated. You have to think about the users, how they interact with it, how those interactions propagate through different components, as well as how to handle error situations gracefully. But things get even more complicated when you start looking at more extensive systems, like those with multiple external dependencies.

Dependencies come in various forms, including third-party modules, cloud services, compute resources, networks, and others.

This level of complexity is standard in almost all projects involving a large organization, whether delivering internal tools or external products.

It means you must put emphasis on developing test systems and mechanisms good enough to validate not just code, but those third-party dependencies as well. After all, they’re part of the final product and failing to interact with them, means the product fails.

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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 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 principles used in managing their solutions.

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Engineering the Tests that Matter

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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