Brijesh Prabhakar who is AVP & Head of Assurance Services at LTI took a Session on "AI in App Dev & Test" at Global Testing Retreat #ATAGTR2019 Please refer our following post for session details: https://atablogs.agiletestingalliance.org/2019/12/04/global-testing-retreat-atagtr2019-welcomes-brijesh-prabhakar-as-our-esteemed-speaker/

1. AI in App Dev &
Test
“You can resist an invading
army, but not an idea whose
time has come”
#ATAGTR2019

2. @BrijPrabhakar
AI is pervasive and becoming more so…
Healthcare
AI assisted diagnosis will
be a $20B Market by
2026 globally
Banking
AI in Banking will be a
$19.8B Market by 2025
globally
Insurance
AI in Insurance will be
worth over $2.6B in
North America by 2025
Transportation
Self driving cars, AI in
traffic mgmt. will be
worth $3.5B by 2023
Retail
AI in retail set to grow to
$5B market by 2022
across the globe
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*PS Market Research, 2018*Statistics MRC, Aug 2019 *Market Watch, May 2019 *ResearchAndMarkets.com, 2019 *MarketsAndMarkets.com, 2018
By 2030 – AI will contribute $15T to the global economy
https://www.pwc.com/gx/en/issues/analytics/assets/pwc-ai-analysis-sizing-the-prize-report.pdf

3. @BrijPrabhakar
So why is AI in App
Dev & Test still
waiting to take off ?
Legacy Mindset
While Agile has
become mainstream,
the AI mindset needs
to evolve
Improper Tools
Current set of AI tools
are only solving point
problems in the dev
lifecycle
Skills Gap
Data Scientists & AI
are only now waking
up to the potential in
the App Dev space
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4. @BrijPrabhakar
How can AI enrich
Agile & Devops?
Extracting Higher Business Value from IT
Investments
Do More
Increased IT
Throughput
Do Less
Reduce Effort Waste &
Redundant Processes
Do Fast
Amplify automation
outcomes
Do Better
Higher Quality & Value
Extraction from SDLC
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> <
^
ai
>>

5. @BrijPrabhakar
A Perspective on App Dev’s Evolution with AI
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6. @BrijPrabhakar
Possibilities of AI in Testing
6
AI in Testing
Test Optimization
Pattern
Matching
Defects
Regression Hotspots
Release defect
prediction
Incidents
& Tickets
Regression Hotspots
Test Data Enrichment
Performance
hotspots
Environment capacity
Planning
Requirements
Test Design
Optimization
Test Data enrichment
Integration & Cross
Application scope
Test Execution
Test Suite
optimization
Regression design
Automation
Opportunity
Data
Co-relation
Requirement to test
cases
Requirement Test
complexity
Story Sizing – Test
Automation
opportunity
Coverage analysis
Requirements to
defects
Application hotspot
Story Sizing - Test
Defects to Incidents
Test Design
Test Data
Code Change
behavior
Defect Injection
Pattern
Developer efficiency
Cross Application
impact
Regression&
Performance Hotspot
Technical Debt
Code change to
Script Objects
Script impact
Test Data Impact
Test Data Pre-set
Test
Acceleration
Automation
Script-less
Auto Generation
Metrics &
governance
Release Management
Self Healing
Object mapping
Page to UI element
mapping

7. @BrijPrabhakar
Sample Use Cases
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Some Use Cases

8. @BrijPrabhakar
Use Case 1
Incorporating AI into
a Devops chain
○ Increase Pipeline Efficiency
○ Faster Deployment Time
○ Lower Number Deployment Steps
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9. @BrijPrabhakar
Use Case 2
Predict Business
Impact due to
Change
○ Reduce dependence on SMEs
○ Provide feedback to dev and architects
○ Algo driven correlation and traceability
for decision making
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10. @BrijPrabhakar
Use Case 3
Is the composition of
the Agile team right?
○ Is the team adequately trained to
contribute to the program?
○ Are we able to drive productivity / sprint
velocity?
○ Are fewer mistakes being made in design
and coding?
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11. @BrijPrabhakar
Use Case 4
○ Resolve new defects based on the
resolution of old defects / production
tickets
○ Use ML for auto-healing if the
correctness of fix is >90%
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Defect Triage & Auto
Resolution

12. @BrijPrabhakar
What will it take to
make AI Pervasive in
App Dev & Test?
Org Design
Include Data Scientists
in Agile Scrum Teams
& Pods
Enrich SDLC Data
Enrich and Learn from
what your SDLC data
tells you
Lifecycle Approach
Enrich Agile & Devops
Platforms with AI
decisioning
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13. @BrijPrabhakar
Thank You
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