Helping Test & Test Automation with AI-ML

"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 8 months ago

Public Feedback

comment Suggest improvements to the Author
  • LBP LBP
    By LBP LBP  ~  1 month ago
    reply Reply

    Mr. Vaibhav Singhal is an outstanding trainer. 
    He always goes right to the point, with practical perspectives, mastering the topic and plain English explanations.
    It would be just a pleasure to learn from him about "AI Helping Testing": state of the art, his own opinions, ideas and approaches.

  • Pallavi ...
    By Pallavi ...  ~  6 months ago
    reply Reply

    Hi Vaibhav

    can you change the outline/structure of the talk to include the case studies you have mentioned and focus more around it. 

    there is a lot of theory in the talk, or so it seems while looking at it, although needed but can we do it more on talking about how AI/ML help in Test, which your title suggest and focus around it. And showcase tools/technologies - case studies to audience for it. 

    let me know what you think. 

    • Vaibhav Singhal
      By Vaibhav Singhal  ~  6 months ago
      reply Reply

      Hello Pallavi, thanks for taking a deeper look into the proposal contents :)

      "AI/ML help in test" name is given considering the idea is not to showcase how AI/ML can help you or many ways they can help you.

      rather to represent the usecase or problem statement as mentioned above and how we resolved our problem using AI/ML. Since talk would be more focussed on this use case and problem statement so it would be demo solution. 

      AI/ML theory is not in scope for this proposal.

       

      thanks

      Vaibhav

      • Pallavi ...
        By Pallavi ...  ~  6 months ago
        reply Reply

        Understood, so can you accordingly please update the outline structure of the talk with time division to present those use cases as well. 

        And if possible can you make that title a bit more specific something like problem solving , so as to differentiate it with other proposals out there. 

        its just a suggestion.

  • Suhas Bharadwaj
    By Suhas Bharadwaj  ~  7 months ago
    reply Reply

    Good topic and helpful for the current trend in the market.

  • Robin Gupta
    By Robin Gupta  ~  7 months ago
    reply Reply

    I believe that most of the content on the outline focuses on machine/deep learning concepts rather than Selenium or Test Automation. Can you please update the deck or the content here to highlight overlapping sections with the theme of Selenium conference?

    • Vaibhav Singhal
      By Vaibhav Singhal  ~  7 months ago
      reply Reply

      Hello Robin, I agree that the topic is not directly related to Selenium but to test automation it is.

      Please check the use cases mentioned in my listed talk description which are related to test reporting and bug analysis. These two features are usually done manually once we receive the test automation result, we present the solution using machine learning concepts how we automated these problems statement. 

      Often we talk about "AI, machine learning etc" but to use them, we have to go to qa tools having AI features implemented for now. Talk is related to mindset how we can start using machine learning to solve our day to day automation/manual testing challenges, whenever needed or required as per your requirement.

       

      regards

      Vaibhav

  • Ravi Muragani
    By Ravi Muragani  ~  7 months ago
    reply Reply

    Great topic Vaibhav. Looking forward to this.

  • Sumit kar
    By Sumit kar  ~  8 months ago
    reply Reply

    This is an interesting topic and very much relevant in today's job scenario. Thanks for bringing this up!

  • Haritha Busaareddy
    By Haritha Busaareddy  ~  8 months ago
    reply Reply

    It seems very interesting talk!!  which i'm waiting for this topic

  • Potru Satish  Kumar
    By Potru Satish Kumar  ~  8 months ago
    reply Reply

    very interesting topic! looking forward to know more about this concept.

  • Ankisha Gupta
    By Ankisha Gupta  ~  8 months ago
    reply Reply

    This sounds interesting and looking forward to read more about this

  • Umesh Kumar
    By Umesh Kumar  ~  8 months ago
    reply Reply

    It seems very interesting talk!!  Looking forward for this. 

  • rama sharma
    By rama sharma  ~  8 months ago
    reply Reply
    Vaibhav is a great trainer. So this is great opportunity   to learn from him about AI

     

    • Vaibhav Singhal
      By Vaibhav Singhal  ~  8 months ago
      reply Reply

      thanks, rama for these encouraging words :)

  • Prabh Preet Singh
    By Prabh Preet Singh  ~  8 months ago
    reply Reply

    I am really looking forward to it.

    • Vaibhav Singhal
      By Vaibhav Singhal  ~  8 months ago
      reply Reply

      thanks Prabh, hope I would meet expectations. 


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