1. MBML_Efficient Testing Methodology for Machine Learning
Sastry Bvssrs Senior IT Quality Analyst
Nitin Upreti Project Manager
Optum

2. ..then the Strategies… First some concepts… ...then Conclusion.
ABSTRACT
Machine Learning is the industry buzz word since past two decades. As the data is spread across in a non homogeneous manner We need to perform effective Testing to ensure the correctness of data and the functionality of the machine which is performing the analytics. Few of the Applications functionality deal with understanding and responding to the properties of data sets and their interactions and validating those applications will be a tedious task for Testing Teams.
This presentation outlines how we can perform Test Efficiency & derive QA strategy by defining Efficient Testing Methodology for Machine Learning.
..then the Strategies…
First some concepts…
Model Based Machine Learning
Business Challenges
What is Machine Learning?
How Data is Spread?
Testing Framework for Machine Learning
(conceptually, Technically, Team & Style)
...then Conclusion.
Benefits to business
Metrics

3. CHALLENGE
In a recent IT Economic survey*, just 62 percent of functional Testing is covered in traditional Model
Performance Testing & security Testing are a barrier in Traditional Testing
Traditional Testing has limitation in terms of complex testing with interactions
Defects are identified at later stages which incurs in huge cost to fix the issue
* - Source: Informa

4. WHAT IS Machine Learning?
Machine learning is the subfield of computer science that, according to Arthur Samuel, gives "computers the ability to learn without being explicitly programmed.“
Source: Wikipedia

5. Traditional Testing

6. How Data is Spread?

7. How Machine Learning is Treated?

8. Deriving Models in Machine Learning

9. Model Representation of Requirements/Modules
Testing Framework
Model Representation of Requirements/Modules
Perform Ranking of various paths to Perform Test Execution
Perform Context Driven Testing
Automation of Modules
Perform Perspective Analytics using R- Language

10. Test Coverage Index Factors Contributing to Test Coverage Index are:
Test Scenarios Generated
Requirement Traceability Matrix Mapping across Requirements
Depth of the Model
Positive & Negative Scenarios
Test Optimization

11. Test Maturity Index Factors Contributing to Test Maturity Index are:
Skill Set of the Team
Trainings Attended
Certifications
Test Planning
Test Design
Test Execution
Defect Management

12. Automation Maturity Index
Factors Contributing to Automation Maturity Index are:
Automated Regression Bed
Usage of Automation Tools
Performance Measures
Automation Penetration

13. BENEFITS TO BUSINESS Efficient Test Case Optimization $$
Structured Testing Approach
Efficient Automation Approach
Automatic Test Case Generation to the exhaustive Level
Less Expensive Rework
Reduction in Production Defects due to early defect Detection
Efficient Analytics as a whole
Effective Test Scenario Mining using Model Paths $$ $
$$$
Model Based Testing provide more options for Exploratory Testing
Speed
Up to 50% increase in speed to market through streamlined software delivery
Test Coverage
Increased Test Coverage and reduced manual Effort due to Model Based Testing
Reduced defects in later test phases by up to 30% and overall higher quality
Operational state is more stable and secure and changes are systematically auditable
DELIVERY RISK
RESILENCY

14. Author Biography
B V S S R S Sastry works as a Senior IT Quality Analyst at Optum Global Solutions, Hyderabad and have 5+ years of experience in Integrated Test Environment Services, ITIL & CMMI Level 5 assessment Process Improvement in Insurance domain. He holds a Master’s Degree in Technology from JNTU Hyderabad. He is a certified ISTQB professional, ITIL V3 certified. He has published 17 International white papers till date.
Nitin Upreti works as a Project Manager at Optum Global Solutions, Gurgaon. He has a vast experience of 14 + years in the IT industry with expertise ranging from White Box testing, Mainframes, Web technologies and transforming Legacy applications into Big data based solutions. He is a certified professional from ISTQB and the Academy of Health care Management, USA. He Holds a Post Graduate Diploma in Business Management along with a Bachelor degree in Computer Applications.

15. Thank You!!!