The cornerstone of the testing is trust. For the test to be
value, we must believe in the output of the test. So how do we believe in
testing the AI?
Vighnesh Sreekanta, chief scientist at test.ai, address this
question in Automation Guild in 2019 with "Test Machina" talk.
experienced AI
As already mentioned, to use AI effective in testing, we
have to get familiar with it. Here are some of the applications and tools that
have nothing to do with testing; people can use this to get a sense of what the
AI does.
Drawing fast!
Okay, this one's fun.
In a presentation Sreekanta described above, it uses
Google's tools. In it, you are challenged to draw objects in 20 seconds. During
that time, the AI try to guess what the picture is.
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One great way to get familiar with something is to play, and
this is a fun tool for it.
taught Machines
Taught Machine is another experiment from Google. This site
allows you to try a training model for machine learning. Classifying video
samples, photos, or sound to train a machine-learning algorithm. Then give it a
try and see how it works.
Of course I had to try this! This site allows using your own
video cam to record images. I made a picture holding a solved Rubik's Cube, the
cube partially solved, and solved cube, all from different angles. Then I
picture are classified into two groups-solved and unsolved.
It was a tremendous job of classifying the new image of the
cubes in the new configuration. Of course, as a tester, I can not help but try
to confuse it, and I did.
This machine is very well taught to quickly set up the
machine to learn and experiment with it. Learn how AI as such can work for you
or against you. If you are using AI-enabled testing now or are considering it,
this would be a great way to get familiar with the ups and downs of ML.
ML is the most unlimited area for exploratory testing I
found.
Know your tools.
Weka
Weka is Waikato Environment for Knowledge Analysis, created
and managed by the New Zealand University of Waikato. Use it to explore the
data with machine learning. ML You can use different algorithms, configure
them, and train them in the dataset.
Weka is the first tool I used to begin to understand ML.
While the user interface has room for improvement, do not judge a book by its
cover. The strength of this tool goes far beyond appearances.
AI AI in Tests and Testing Meetup - Santa Clara, California
Meetups are a wonderful way to learn, and because of the
pandemic, most meetups are now virtual, which means you can join them from
anywhere.
The only AI in testing meetup I find is one of the hosts in
Santa Clara, California. Last month, they talk about "Creating Deployable
Classifier uses Tensorflow Jira Bug." I can not wait to see what's next
for this group!
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AI tool
The most AI tool is a commercial, for several reasons. Total
team effort put into building the AI tool for testing very large, and gifts
to customers can be greater than the price many times. Kevin Surace, CTO of
Appvance, explains that his company AI tool consists of about 4 million lines
of code and took about 250,000 engineering hours to develop.
I believe there will be a lot of AI-based, test-help tool in
the open-source market over the next few years. But for now, here are some
tools that can help.
Test.AI Classifiers
Test.ai classifier using machine learning to match elements
on a web page. These classifiers are available in a number of different
languages.
TensorFlow
Want to use machine-learning API to test your own ideas?
TensorFlow is one API to get in there to quickly apply the model ML.
AGENT
AGENT is an abbreviation of "AI Generation and
Exploration in the Test." Search this bot generator to test your site on
GitHub repo Raja Tariq.
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