I see so many people lately talking about the “death of manual testing.” Opinions obviously polarize on this but what I don’t see is testers engaging at all with why this perception is there. There is a form of indoctrination that happens across the industry. And testers, by and large, do nothing to combat it. Largely because they ignore where it’s coming from. Let’s talk about this a bit.
The Tester Role in Machine Learning, Part 4
This is the last of a four part series (see parts 1, 2 and 3). The goal has been investigating whether specialist testers have a role in machine learning environments uniquely distinct from development roles in those same environments. These posts have been getting you up to speed on what that might look like. Here we finish off that journey.
Continue reading The Tester Role in Machine Learning, Part 4
The Tester Role in Machine Learning, Part 3
This post continues on directly from the first and second parts. I covered a lot of material in those posts so I can’t easily recap it here so definitely read those before reading this one. Here we’ll dig more into how a tester actually tests in this context but also look at testing as a framing activity.
Continue reading The Tester Role in Machine Learning, Part 3
The Tester Role in Machine Learning, Part 2
This post continues on directly from the first one in the series. We’ll take the CartPole example we started with and continue our journey into how testing — particularly that done by a specialist tester — intersects with the domains of data science and machine learning.
Continue reading The Tester Role in Machine Learning, Part 2
The Tester Role in Machine Learning, Part 1
As a tester are you ready to work in environments that are based in or around data science and machine learning? What will you actually do in these environments? How will you interact with developers? How technical do you have to be? Is it all just automated testing? Or do we still have room for a human in there somewhere? Let’s dig into this a little bit by going through a scenario.
Continue reading The Tester Role in Machine Learning, Part 1
The Architecture of a Micro-Framework
In a series of posts, I’ve talked about my Tapestry micro-framework and I tried to provide some of the rationale for its design choices. Providing that rationale meant providing a context for you to see it in action. This post will cap off the previous posts by digging into the code of Tapestry a bit and showing you how it works. I hope this is more relevant given that you’ve now seen it in action.
Micro-Framework Context Patterns
In the previous post I talked about communication patterns in terms of the micro-framework and tests. Here I’ll talk about the expressiveness of the tests themselves, showing how Tapestry supports the idea of a context.
Micro-Framework Communication Patterns
In my last post on micro-frameworks, I got into the organizing principles of my Tapestry solution, by which the framework provides or supports a mechanism for the encapslation of and delegation to logic. Here I’m going to continue on that theme but with a focus on showing how the framework calls into the tests, rather than the reverse, and why I think this is a good design approach.
Organizing Principles for Micro-Frameworks
This is a continuation of my exploration into providing insight into micro-framework creation for automation, using my own Tapestry tool by way of example. The first post set the context and the second post focused on exposing an API. Here we’ll dig into exposing the organizing principle.
The API of a Micro-Framework
Here I’ll continue on with the introduction of my Tapestry micro-framework that I started in the first post. This time I’ll focus on a bit on how you want to create an API interface for your micro-framework.
Writing Automation Micro-Frameworks
Here I want to talk a little about test automation framework construction. Or, rather, micro-framework construction. I will use my own tool, called Tapestry, for this purpose. Tapestry is written in Ruby but what I talk about is potentially transferrable to your language of choice.
Testing and Machine Learning, Part 2
This post continues on from the first part where I went over the high-level details of a tester getting involved in a machine learning context. I left off just at the point of introducing the algorithm and letting us get to work. So here, in this post, we’re going to dig right in.
Testing and Machine Learning, Part 1
I’ve talked before about the intersection of testing and AI as well as provided a series of posts, using a Pac-Man clone to further introduce testers into algorithmic searching. Here I’ll consider a really simple example of engaging with a machine learning example. I’ll focus on reinforcement learning, which often isn’t talked about as much.
About Testing
Earlier I talked about describing my own role. I think what I said there is almost interesting. Interesting, for me that is. But I often find testers struggling to frame their value beyond “I find bugs” or “I help mitigate risk.” So let’s dig into this a little bit.
Technical Test Interviews are Broken
Here I’m not speaking to the people who are interviewing for roles in automation. I’m speaking to the people hiring them. The interview process is entirely broken in so many places. According to Eric Elliot, code-based interviews have always been broken. And he’s probably right. Sahat Yalkabov said something similar. He’s probably right too. But here I’m focusing on the companies and hiring managers that are exacerbating the technocrat problem. So let’s talk about this.
Testing vs Checking – A Flawed Argument?
Lately I’ve been seeing that the whole “testing” vs “checking” debate is now more used as a punchline than it is for any serious discussion around testing as an activity and tests as an artifact. Regardless of my perception, which may not be indicative, I believe that this distinction has not been very helpful. But let’s talk about it. Maybe someone will convince me I’m wrong.
When Do You Stop Testing?
The question of this blog title comes up often. The worst answer that can be given is: “When there are no more bugs.” It’s the worst answer because the inevitable follow up is: “But how do you know?” On the other hand, some people, upon answering this, begin providing a very convoluted answer. Here’s my take.
Testing Fundamentalism?
I phrase the title of this article as a question. This will be a short article. I don’t have solutions. I’m not even sure I have a problem. But I think I do. I think I’m sensing a problem based on observations. But maybe not. Let’s see if you agree or disagree.
Uncomplicate TDD and BDD
Similar to reframing agile, I encounter a (perhaps surprising) number of experienced developers who outright deny that approaches like TDD or BDD have any value and it seems like we need a reframing here. But, in fact, I’ve found it’s more a case of getting people to uncomplicate the ideas.
Reframing Agile
Lots of people seem to focus on whether agile has failed. Or whether it’s dead. Or whether it’s a methodology. Or a process. What you end up with is something akin to Edmund Burke’s denunciation of political factionalism: “tessellated pavement without cement.” In the testing world this is even more so the case given the oft-used phrase “agile tester”, which any test specialist should be against. So let’s talk about this.