I was very fortunate that I was able to attend my second ever Test Bash in Manchester. This year was better than last year as two of my co-workers (Hannah & Jack) came along for the ride. I got so excited seeing them get excited!

I spent most of the conference day scribbling notes again. However unlike last year where I mostly wrote text in a pad. This year I had plain paper and used coloured pens. At the open space the following day it was really nice to have my notes from the conference day to hand. In the days following the conference these visual reminders really helped important ideas stick in my head.

I sent all my visual notes up into the Twitter-verse as they were completed. List of tweets below.

Anne-Marie Charrett @charrett Quality != Testing

Goran Kero @ghkero What I, A Tester, Have Learnt From Studying Psychology

Gem Hill @Gem_Hill AUT: Anxiety Under Test

Bas Dijkstra @_basdijkstra Who Will Guard the Guards Themselves? How to Trust Your Automation and Avoid Deceit

James Sheasby Thomas @RightSaidJames Accessibility Testing Crash Course

Vera Gehlen-Baum @VeraGeBa Turning Good Testers Into Great Ones

Simon Dobson Lessons Learnt Moving to Microservices

Martin Hynie @vds4 The Lost Art of the Journeyman

Claire Reckless @clairereckless The Fraud Squad - Learning to manage Impostor Syndrome as a Tester

Michael Bolton @michaelbolton Where Do you Want To Go Today? No More Exploratory Testing

Twitter Mining

Last year, I did some Twitter mining and sentiment analysis after the event. I wanted to re-use those scripts again to tell this year’s story. After I got home (and had a bath and good rest) I sat down with my laptop and mined 2700 tweets out of Twitter on the hashtag #testbash. I worked through my code from last year starting to piece together the story of this year’s event. If you’re interested in the code that this article is based upon, it can be found (along with the raw data) here on Git Hub

Positive and negative word clouds

The word clouds above can be clicked for a larger image. The first thing I noticed after generating some positive and negative word clouds was that the positive cloud was bigger than the negative cloud. 173 unique positive words and 125 unique negative words were identified in the conference day tweets. The conference was a resoundingly positive event!

It didn’t surprised me that the word ‘Great’ was at the center of the positive word cloud. Having done this kind of text crunching a few times now I’ve learned that ‘great’ and ‘talk’ are generally two of the most common words tweeted at conference events. What did surprise me though was the negative word cloud. Right at the center, the most frequently used negative word ‘syndrome’ closely followed by ‘anxiety’. Claire Reckless & Gem Hill spoke about imposter syndrome and anxiety. Both these talks had a huge impact on the Twitter discussions which were taking place on the day. Getting the testing community talking about imposter syndrome and anxiety, even though the words used carry negative sentiments, is a very positive outcome.

The top 5 most favourited tweets were:

#1

#2

#3

#4

#5

Tweets by Time and Positivity

A number representing positivity index was calculated for each tweet. For every word in the tweet present in a dictionary of positive words, the tweet scored +1. For every word in the tweet present in a dictionary of negative words, the tweet scored - 1. The positive and negative words list used to score tweets was created by Minquing Hu and Bing Liu at the University of Illinois and can be found here

The tweet with the most positive sentiment on the day was this one from Richard Bradshaw

The tweet with the most negative sentiment on the day was this one from Dan Billing.

I plotted all the tweets by time and positivity then fitted a loess curve through the points on the scatter plot.

The first thing that really stood out was that one tester was up, awake and tweeting a picture of the venue at 4:17am?!?

Once the event got started, there was a dip in positivity just after 10:00am - Checking some of the tweets around that time

Reason for the dip is related to tweets about bias.

There was another dip in positivity just after 16:00 so I checked those tweets too.

Again, nothing negative was happening, the dip in positivity was caused by the discussion of a subject which has a negative sentiment.

Really positive tweets came at the end of the day once the event had been absorbed. With the last part of the day carrying the most positive sentiment

Tweets by Frequency and Platform

I plotted a frequency polygon broken down by platform to see which parts of the day people engaged the most with Twitter. Again the image below can be clicked for a larger version.

It was very interesting to see how frequently people were tweeting through out the day. The spikes in activity align very closely with the start of each talk. It was also nice to see people taking a break from using twitter on mobile phones over lunch (hopefully this is because real face to face conversations were happening over a meal). The biggest spike of activity happened immediately after lunch time was over during Vera Gehlen-Baum’s talk “Turning Good Testers Into Great Ones”.

It was a pleasure connecting so many wonderful people at this event. The mix of new faces and familiar faces was fantastic. Test community is the best community ♥ Hopefully see you in Brighton next year!

This post was originally published on my software testing blog Mega Ultra Happy Software Testing Fun time.

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