1. Machine Learning Telepathy for Shift Right Approach
Santhosh Gadamshetty (Sr Quality Engineer)
Shashikala Hugar (Sr Quality Engineer)
Allscripts India Pvt Ltd

2. Abstract Product Released, what next? Collect Data (SHIFT )
Analyze & Predict (Machine Learning)
Future Readiness with Actions
Testing in early phase is recommended but not enough, Continuous feedback from customer is import and let us see how automatically can generate the feedback

3. How can we Prevent/Answer these types of Questions ?
Client reports Case.
Why are only some clients reporting it?
Is it a environment issue?
Is this part of frequently used workflow?
How Clients are using new feature
How can we Prevent/Answer these types of Questions ?

4. Machine learning Shift Right
Computers can learn on its own without writing logic for all the cases based on data classification
Shift Right
Approach Says Continuous feedback from customer is important even after releasing Quality product

5. Needed V/s Asked Shift from KYC to LYC
Know your Customer (KYC) to fulfil what customer asks
But with Machine Learning enables
Learn your Customer (LYC) to predict what customer needs
Shift from KYC to LYC

6. Dynamic Baseline vs Manual Threshold
Supervised Machine learning: Algorithms system can forecast the performance growth and Predict probable Future performance bottlenecks .
Get Ready Before
Customer ASKs !!

7. Select Interested Data Predict the Performance Insights to Development
Supervised Learning
Select Interested Data
Create and Train Model
Predict the Performance
Insights to Development
Plan for Next Release

8. UX Improvements With ML
Unsupervised Machine learning
Algorithms system can predict the most common navigation pattern of the users.
Results help Agile teams improve the client user
User experience on realistic
values
UX Improvements With ML

9. Intent Vs Reality Take action!!
Generate usage analytics of new features and predict future usage growth with help of Machine Learning.
Take action!!

10. ML Applications for Beta Feedback
Record Feedback
Convert to Text (Google Speech API)
Analyze the Text (Google Natural Language API)
Analyze Verbs, Nouns
Generate report
With Google Machine Learning API

11. Popular ML Algorithms Linear Regression Algorithm
Polynomial prediction Algorithm
Exponential prediction algorithm
Decision tree learning

12. ML Platform

13. Case Study Following studies are in progress
Generate Real time performance predictions from all the clients – Helps to create dynamic baseline and bottlenecks
Insights of Client workflow and experience with software – Help us to predict the client use cases

14. Case Study Contd..
Feature usage analytics and predict - Helps in planning the next releases

15. Key Takeaways Shift from KYC to LYC
Certified and Release software is not Definition of Done.
Learn and Generate Future requirements
Get Ready for future with Statistical Analysis of ML

17. References

18. Author’s Biography Santhosh : Shashikala :
Specialist in Automation and Manual Testing and working as Technical lead for QA. Certified Scrum Master from scrum alliance, Expert in Healthcare domain and Banking domain. Around 9+ years of experience in software Quality Assurance. Worked on different technologies such as Web, Desktop, API, Mobile and Healthcare Devices, Proficient in security testing. Presented White paper in Software quality forum.
Shashikala :
An Expert in software test process with an 8 years of experience, Expert in Healthcare domain, Worked on various technologies including the mobile apps, Linux and Windows applications.