The title of this post indicates a sentiment you’ll often see. “Testing is the art of …” and then fill in some word or phrase. While I get the intent behind this, the word or phrase used to complete the sentence is often a bit lacking and actually reinforces the opinion that many have of testing, which is that it’s not a distinct enough discipline.
This post is the last in the data science series (see 1, 2, and 3). Here we’ll wrap up the entire discussion, including whether we actually discovered anything about our initial question regarding “Which is the best Pokémon character?”
This post follows on from the first and second posts. Here we’ll continue the theme of our Pokémon data, this time working at getting some more specific answers to our running question in this series: “Which is the best Pokémon character?”
This post follows on from the first. Here we’ll focus on the skill of interpreting our Pokémon data so you trust the data enough to make decisions based on it. This is fairly important if you are a tester working in any context wherein actions against data will be presented to you or where you have take those actions yourselves as part of the testing.
I’ve talked a bit about testers and AI as well as testers and machine learning. Here I want to focus a bit on one area that can be a basis for both of those areas: data science. As a tester, you don’t need to be a data scientist. But it certainly doesn’t hurt to have a grounding in what data scientists do. Here we’ll do some exploratory computing with Jupyter; we’ll use some numerical and visualization libraries and we’ll explore the (fascinating?) world of Pokémon data. So let’s take a few posts to dig into this.
What do people tend to think of when they hear “tester”? More specifically, what do they think a person called “tester” primarily does? Arguably, more often than not, you’re going to hear something like “a tester’s role is to find bugs” or “a tester helps surface issues.” As an approximation, that may be accurate. But it’s only a very rough approximation. And a dangerous one. So let’s talk about this.
As part of my attempt to continue my thinking on the topic of “tests” and “checks”, I’m revisiting what I talked about previously. I want to see if my thinking has changed and I want to set myself up for being willing to change my opinion. I don’t want to be one of the fundamentalists I wrote about. So let’s take another trip down the rabbit hole.
I believe there’s an interesting parallax effect that happens in conversations around testing and the implementation of testing ideas. This is an area I want to investigate a bit more so I’ll start off as I usually do: with my half-formed ideas as the basis for discussion, elaboration and possible refutation.
… but you may not be a tester. Or at least not a specialist tester. Here I’ll close out 2017 on this topic which will indicate some of my future direction in 2018.
Dan Ashby recently posted his views regarding a model for test strategies and heuristics. I really like the level of effort put into it, particularly because it was clearly distilled not just from thought but thought based on experience. Let’s talk about this.
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.
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.
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.
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.