"To implement or use AI (mostly creating a solution to help in testing) you don't need to be an expert or some certified data scientist etc. My talk revolves around how being a normal automation tester, we see some challenges and with limited knowledge, we start leveraging AI to help in our test. Talk create a mindset and case study how AI may help you in some of your day to day challenges in testing."

The impact of AI has penetrated our lives and increasing daily. Related to AI-ML existence in Testing, two scenarios are:

- Testing AI programs

- AI helping Testing

This talk is related to later aspects of "AI Helping Testing". There are a number of possible ways how it is impacting and many companies are already working on developing tools around same and many test solutions are already available in market.

Whenever AI keywords are intercepted, the common image is of "autonomous car, robots" and then the question, how these cars and robots will help us in testing, rather the thought should be these the outputs of AI.

AI/ML can be leveraged in the number of areas and scenarios to solve and help in our day to day testing activities. This talk would discuss about AI/ML, its impact, some existing solutions available, doing brainstorming ideas, so you can identify in your project. Also, USE CASE how we took AI benefit to solve our Automation problem.

Use Case - Problem Statement

1- Multiple automation suites running daily and sharing reports. Each report is having some failures. To do defect triaging for multiple failures is difficult.

Solution: Displaying consolidated reports of actual and new failures suggested by the prediction model (qa analysis on failures was reduced by 80%) based on classification & Deep learning..

2- Auto analysis while bug reporting directly to bug management tool.

Solution: Model predicting the defect is already raised in bug management tool, as per the score it would take appropriate action to create new, update, no action, only notification to team etc.

Talk includes below takeaways:

1- Understanding how is AI/ML/Deep learning specifically in software testing.

2- Brainstorming how to leverage AI to help in your tests.

3- Initial steps to start for any model.

4- Tools to leverage

 
 

Outline/Structure of the Case Study

  • Understanding AI/ML
  • Popular AI solutions in Test
  • AI Unexplored Areas in Test
  • Case Study - Problem Statement
  • AI Prediction Path
    • Finalizing the expected data which solves Problem Statement
    • Identifying it is AI or Multiple Approach
    • Brainstorming data availability
    • Categorizing under supervised or unsupervised learning
    • Programming Model to learn (Classification, Clustering, Regression, Deep Learning ...)
    • Cleansing data
    • Model preparation and prediction
    • Model feedback & adjustment
    • Model as a service
    • Quantitative analysis of this implementation
  • Overview of other case studies

Learning Outcome

  • Understanding of AI-ML and how it's affecting in testing
  • Sharing popular AI-ML solutions on which current IT in working
  • Knowledge how AI-ML is working in our testing industry.
  • Implementation demo of AI solution for our customized need we needed in our project.

Target Audience

Anyone having basic knowledge of what is testing, and why we do

schedule Submitted 1 year ago

Public Feedback


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