In my first post, I talked a bit about why I decided to start this blog. I often get asked how I ended up in testing given my previous career seems so different, so I thought I would step back a few years and talk about what made testing such a good fit for me.
Before my first job in software testing, this is where I used to work:
Or at least, that’s where I worked at least a few weeks out of the year while I was collecting data for my research. Before software testing, I was as astrophysicist.
My research involved using the Giant Metrewave Radio Telescope — three antennas of which are pictured above — to study the distribution of hydrogen gas billions of years ago. I was trying to study the universe’s transition from the “Dark Ages” before the first stars formed to the age of light that we know today. Though I didn’t know that what I was doing had anything to do with software testing (or even that “software testing” was its own thing), this is where I was honing the skills that I would need when I changed careers. There are two major reasons for that.
To completely over simplify, the first reason was that I spent a lot of time dealing with really buggy software.
Debugging data pipelines
At the end of the day we were trying to measure one number that nobody had ever measured before using methods nobody had ever tried. That’s what science is all about! What this meant on a practical level was that we had to figure out a way of recording data and processing it using almost entirely custom software. There were packages to do all the fundamental math for us, and the underlying scientific theory was well understood, but it was up to us to build the pipeline that would turn voltages on those radio antennas into the single temperature measurement we wanted.
With custom software, of course, comes custom bugs.
A lot of the code was already established by the research group before I took over, so I basically became the product owner and sole developer of a legacy system without any documentation (not even comments) on day one, and was tasked with extending it into new science without any guarantee that it actually worked in the first place. And believe me, it didn’t. I had signed up for an astrophysics program, but here I was learning how to debug Fortran.
I never got as far as writing explicit “tests”, but I certainly did have to test everything. Made a change to the code? Run the data through again and see if it comes out the same. Getting a weird result? Put through some simple data and see if something sensible comes out. Your 6-day long data reduction pipeline is crashing halfway through one out of every ten times? Requisition some nodes on the computing cluster, learn how to run a debugger, and better hope you don’t have anything else to do for the next week. If I didn’t find and fix the bugs, my research would either be meaningless or take unreasonably long to complete.
The second reason this experience set me up well for testing was that testing and science, believe it or not, are both all about asking questions and running experiments to find the answers.
Experiments are tests
I got into science because I wanted to know more about how the world worked. As a kid, I loved learning why prisms made rainbows and what made the pitch of a race car engine change as it drove by. Can you put the rainbow back in and get white light back out? What happens if the light hitting the prism isn’t white? How fast does the car have to go to break the sound barrier? What if the temperature of the air changes? What happens if the car goes faster than light? The questions got more complicated as I got more degrees under my belt, but the motivation was the same. What happens if we orient the telescopes differently? Or point at a different patch of sky? Get data at a different time of day? Add this new step to the data processing? How about visualizing the data between two steps?
When I left academia, the first company that hired me actually brought me on as a data engineer, since I had experience dealing with hundreds of terabytes at a time. The transition from “scientist” to “data scientists” seemed like it should be easy. But within the first week of training, I had asked so many questions and poked at their systems from so many different directions that they asked if I would consider switching to the test team. I didn’t see anything special about the way I was exploring their system and thinking up new scenarios to try, but they saw someone who knew how to test. What happens if you turn this switch off? What if I set multiple values for this one? What if I start putting things into these columns that you left out of the training notes? What if these two inputs disagree with each other? Why does the system let me input inconsistent data at all?
I may not have learned how to ask those questions because of my experience in science, but that’s the kind of thinking that you need both in a good scientists and in a good tester. I didn’t really know a thing about software engineering, but with years of teaching myself how to code and debug unfamiliar software I was ready to give it a shot.
Without knowing it, I had always been a tester. The only thing that really changed was that now I was testing software.
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