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#ATAGTR2019 Presentation "AI in App Dev & Test" By Brijesh Prabhakar


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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:

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#ATAGTR2019 Presentation "AI in App Dev & Test" By Brijesh Prabhakar

  1. 1. AI in App Dev & Test “You can resist an invading army, but not an idea whose time has come” #ATAGTR2019
  2. 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 2 *PS Market Research, 2018*Statistics MRC, Aug 2019 *Market Watch, May 2019 *, 2019 *, 2018 By 2030 – AI will contribute $15T to the global economy
  3. 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 3
  4. 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 4 > < ^ ai >>
  5. 5. @BrijPrabhakar A Perspective on App Dev’s Evolution with AI 5
  6. 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. 7. @BrijPrabhakar Sample Use Cases 7 Some Use Cases
  8. 8. @BrijPrabhakar Use Case 1 Incorporating AI into a Devops chain ○ Increase Pipeline Efficiency ○ Faster Deployment Time ○ Lower Number Deployment Steps 8
  9. 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 9
  10. 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? 10
  11. 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% 11 Defect Triage & Auto Resolution
  12. 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 12
  13. 13. @BrijPrabhakar Thank You 13