Reducing the Scope of Load Tests with Machine Learning

Hello hello,

Today I am going to share this really interesting webinar of my friend Julio de Lima, he is one of the top QA influencers in Brazil, and in this video he is talking about how to reduce the scope of load tests using Machine Learning.

Load testing execution produces a huge amount of data. Investigation and analysis are time-consuming, and numbers tend to hide important information about issues and trends. Using machine learning is a good way to solve data issues by giving meaningful insights about what happened during test execution.

Julio de Lima will show you how to use K-means clustering, a machine learning algorithm, to reduce almost 300,000 records to fewer than 1,000 and still get good insights into load testing results. He will explain K-means clustering, detail what use cases and applications this method can be used in, and give the steps to help you reproduce a K-means clustering experiment in your own projects. You’ll learn how to use this machine learning algorithm to reduce the scope of your load testing and getting meaningful analysis from your data faster.

 

Thank you Julio šŸ™‚

Machine Learning in IOS apps

Hello guys,

I have been to the #TechKnowday here in London three weeks ago and one of the workshops that I joined was the Machine Learning in IOS Apps. So, in the end we had a face recognition app which showed what was the emotion of the person, if the picture was someone smiling than it should show the smile emoticon and so on.

You can find the slides and follow the explanations here:

https://github.com/costescv/MachineLearning/blob/master/MachineLearning.pdf

Then you will need to clone the repository with the projectĀ https://github.com/costescv/machinelearningĀ and download the Sentiment Polarity model here

Step 1:

In theĀ ViewController.swift you will need to create the face detection request, the request handler and the face detection action. So you will have something like this:

 

Step 2:

In theĀ SentimentAnalysisService.swift you will need to create the model using the SentimentPolarity, pass the linguistic tagger options and create the input to receive and interpret the input with the sentiment. You can add, remove or change the sentiments in theĀ Sentiment.swift class, but don’t forget to change in this class the sentiment as well.

 

 

So, after you build, run the app and type the name of the sentiment with a space in the end, so you should have something like this:

 

ThanksĀ Vasilica for this workshop !