Shift-Left With AI/ML (Quality Intelligence)

In today's quality assurance space, the most popular coinage is AI in Quality Assurance".

The intent or thought behind is to speed up or reduce our testing cycles and ship our product faster to the market. 

In this article, I would like to showcase what industry is expecting us (Test Engineer's) in 2020 to achieve as part of "AI in Quality Engineering". Along with that how could leverage ML (Machine Learning) and achieve a Shift-Left towards design and development. 

Industry on 'AI in Quality Assurance'

Let us see, some of the trending tools in the respective classifications 

• Visual Testing 
  • Commercial — Applitools
  • Open Source — PhantomCSS
• Continuous Testing
  • Commercial — XRAY, ZEPHYR , Test Rail, qTest
  • Open Source — Customized Integration with Testlink 
• Predictive Analysis
  • Commercial —  Sauce Insights
  • Open Source — ReportPortal.io
• API Coverage
  • Commercial —  SeaLights, ReadyAPI 
  • Open Source — Not Sure if any exists
• Scalable Tests and Extended Coverage 
  • Commercial —  Sauce Labs, Browser Stack, pCloudy
  • Open Source — Zalenium, Selenium Grid With Kubernetes clusters

My POV (Point Of View): Quality Intelligence 

SHIFT – LEFT has its own advantages, bringing Artificial Intelligence to this approach, make it more powerful and easier for companies to implement. 

I believe in this thought/approach and when we integrate with time series databases we could ensure we “FORECAST” and drive application development with measured and actionable quality. 

Sample Prediction of Test Cases Success 

I would like to conclude by stating that 'Predicting and Prioritizing'  through Time Series modeling like AR(p), Holt's Moving Average, ARIMA(p,d,q), and Exponential Smoothing, etc. with respect to your application data would ensure we gather measured and actionable information about the quality of our application [Quality Intelligence].