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.
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.
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.
I talked before about tradition and dogma and not too long ago, on LinkedIn, I saw someone post yet another one of those bits of dogma in our industry without considering the context. The discussion that ensued showcased exactly the problem with simply regurgitating the “received wisdom” of others. So let’s talk about this.
For this post I’m going to be giving you all the commands you need to run algorithms through Pacumen. But I will note that the readme file of the Pacumen project does provide some context for you should you choose to play around with the project. You can also reference my exploring testing and AI post for more details on how to use Pacumen.
This post follows on from the first part. That post, and this one, are building up your understanding of the algorithms that you have to consider when you are a tester in a machine learning or AI environment, particularly when dealing with search problems.
The next two posts are follow-ons from the previous. The goal here is to get you set up thinking as a tester in a machine learning environment and specifically in the context of search problems. This post, and the next, will focus on making sure you have the basics of the algorithms.
Let’s continue the from the last post where you saw a working implementation of a learning environment called Pacumen. Here I want to provide you more details of the basis for this kind of work and use that as a springboard for thinking about how testers fit in these situations.
In this post I want to set the stage for some future posts regarding thinking about how you might work, as a specialist tester, within the context of an environment that is using machine learning and various artificial intelligence techniques. This is an area that I’m finding many testers are not ready for. To that end, I’m going to show you how to get my Pacumen code repository up and working. Then I’ll take you through a few exercises to put it through its paces.
What’s been interesting in the testing world — at least the part of it that I hang out in — is the application of different AI-based learning algorithms to the act of exploring an application and seeing what (if anything) that tells us regarding the algorithmic and non-algorithmic parts of the testing discipline. Let’s talk about this because I think is fertile ground for testers to be exploring.
The question periodically comes up as to what the difference is between “Quality Assurance” and “Testing.” And a disturbingly large number of test professionals will say that “Testing is a subset of quality assurance.” This is a terrible response. Let’s talk about that.
This post continues on from part 2. If you’ve gone through the prior posts in this series, you have a fully functioning package. Now we’ll distribute that package.
Continuing on from part 1, we now have a nice little package that we wrote. Let’s refine this package to be a little more in line with Python practices, add some tests (well, a test), and provide some console execution.
It’s been awhile since I tackled anything too traditionally “technical.” Lately I’ve encountered many testers who are interested in using Python as their ecosystem of choice for test solutions, particularly in data science or machine learning environments. So here I’ll talk about being a test solution developer in a Python context and what it means to create solutions in this ecosystem.