Successfully reported this slideshow.
We use your LinkedIn profile and activity data to personalize ads and to show you more relevant ads. You can change your ad preferences anytime.

Code to Release using Artificial Intelligence and Machine Learning

536 views

Published on

by Nataraj Narayan, Managing Director, AutonomIQ at STeP-IN SUMMIT 2018 15th International Conference on Software Testing on August 31, 2018 at Taj, MG Road, Bengaluru

Published in: Technology
  • Be the first to comment

  • Be the first to like this

Code to Release using Artificial Intelligence and Machine Learning

  1. 1. The Future of AI in Software Development Confidential - Do Not Distribute 19/19/2018
  2. 2. Confidential - Do Not Distribute 29/19/2018 The Application Landscape Has Grown Complex Monolithic Applications running on big-iron server hardware Monolithic and Distributed Applications running on distributed hardware 90’s and 2000’s Mid-2000’s Today and FutureMonolithic Monolithic Distributed ~100s of applications ~10s of applications ~100s apps, SaaS and services
  3. 3. Confidential - Do Not Distribute 39/19/2018 SAAS, PAAS & IAAS – Today’s world of cloud services SaaS PaaS IaaS
  4. 4. Confidential - Do Not Distribute 49/19/2018 Revenue recognition in SAAS – paradigm difference
  5. 5. Confidential - Do Not Distribute 59/19/2018 Deployment Velocity Has Grown Exponentially - Faster time to deliver and higher value “we have gone from 5 deployments per week last year to 80 deployments per week this year” - DevOps @ large insurance
  6. 6. Confidential - Do Not Distribute 69/19/2018 Creating Less Time to Manage Change Monolithic, Distributed, SaaS and Micro-service applications running on cloud Today and Future ~100s apps, SaaS and services LESS TIME TO IDENTIFY FIXES Testing and quality is overlooked at the expense of velocity POOR QUALITY RELEASES Business wants to focus on delivering meaningful outcomes to stakeholders, not putting out fires in the process UNABLE TO KEEP UP WITH THE CHANGES Software economy, and the “uber” moment is disrupting every business Monolithic Distributed SaaS & Micro-services
  7. 7. Confidential - Do Not Distribute 79/19/2018 Autonomous Technology Will Be Key in Delivering Value By 2020, DevOps initiatives will cause 50% of enterprises to implement continuous testing using frameworks and open- source quality tools. This has significantly created the need for new age tools to evolve Organizations seeking to improve their delivery capabilities quickly and that no one vendor’s tools cover the entire delivery pipeline With enterprises aspiring to be digital, autonomous technology is not perceived as a fringe investment but as a key element of the digital journey
  8. 8. Confidential - Do Not Distribute 89/19/2018 So What Does the Software Development Lifecycle Look Like Today? • Most of the software testing lifecycle remains manual • Without Automation, QA is forced to be reactive instead of proactive Status of Quality Automated Manual Requirements Test Plan Test Cases Test Scripts Test Data Test Environment Test Execution Defects Results
  9. 9. Confidential - Do Not Distribute 99/19/2018 DevOps Definition DevOps = Development + Operations Dev Ops Prerequisites:  Automate everything: test, build, deployment, migration, rollback, …  Everything is code: infrastructure, config, environment, schemas, apps, …  Bring development and operations closer together
  10. 10. Confidential - Do Not Distribute 109/19/2018 Testing Remains the Biggest Bottleneck 8 Developers per 2 Week Sprint  640 Total Hours ~ 50 Functional Test Cases per Sprint 3-5 Hours to Create and Maintain Each Test Cases 150 – 200 Hours Total Spend Scripting Time Spent Scripting 31% Other Development Activities 69% Code Commit Build Test Case Creation Test Script Creation Test Data Generation Test Execution Code Promotion Code Commit to Production Centers Around QA 20% 25% 27% 30% 31% 47% 52% Test Data Management Monitoring Code Development Code Reviews Deploying to production Planning Testing Testing Creates the Most Delays in the Development Process1 1. Source: Gitlab Developer Survey 2018
  11. 11. 9% 23% 34% Continuously Deploy to Production Continuously Deploy to Labs Continuously Integrate Software Changes Continuous Processes Remain a Dream 8% 45% 47% Cost Reduction Time to Market Quality Quality is the Top Release Priority for Enterprises Confidential - Do Not Distribute 119/19/2018 Organizations Have Failed to Keep Up 22% 44% 53% 30% 47% 64% Security Testing Integration Testing Functional Testing Manual Automated Companies Rely on Manual Testing While Automation Falls Short Source: voke Market SnapshotTM Report: Release Management
  12. 12. Confidential - Do Not Distribute 129/19/2018 Autonomous Testing Solves Quality Problem automate discovery of your landscape detect changes and execute actions continuously learn and improve any SaaS application any Web application any API or micro-service Autonomously Test, Release and Deploy software NLP Engine Symbolic Representation Engine aIQ Base Model aIQ Learning (Supervised, Unsupervised, & Active) aIQ Testing Neural Database Plan Execute Analyze Test Case Test Script Test Data Sensing & Analyzing Deciding Controlling Testing
  13. 13. Confidential - Do Not Distribute 139/19/2018 automate discovery of application landscape detect changes and execute actions continuously learn and improve any SaaS application any Web application any API or micro-service Any User Existing Environment CI/CD Tools Plan Execute Analyze All Testers Developers Business Analysts Autonomously Test, Deploy, and Release Applications Test Case Test Script Test Data Cross Platform Cross Browser Continuous Change Impact Pattern Matching Dynamically Generate Test Data Using AI Continuously Execute Cross-Browser & Cross- Platform Deploy in the Cloud or On-Premise Integrate with DevOps & CI/CD Tools Autonomously Generate Automation From Existing Test Assets Create New Automation at the Click of a Button Self Heal Automation As Application Changes Seamlessly Maintain Automation as Test Cases Change
  14. 14. Confidential - Do Not Distribute 149/19/2018 Why Autonomous Testing Eliminate traditional bottlenecks to empower development teams with the ability to dynamically train software to deliver AI created test cases, AI created test scripts, AI generated test data, and AI defect reports. Leverage AI to redefine IT processes, from QA to cybersecurity, while seamlessly managing complexity through a scalable, maintainable platform Cut across the enterprise to reduce total cost of quality, accelerate time to value, and provide accountability for end to end business process
  15. 15. Confidential - Do Not Distribute 159/19/2018 Customer Transformation Manually Writing and Maintaining Test Scripts Cloning, Masking, and Subsetting Test Data Sporadic Test Execution Across Disparate Tools Poor QA Reporting, No Metrics for Improvement Script-less Testing Dynamic Data Generation Continuous Test Execution Automatic Reporting, Real Time Defect Resolution
  16. 16. Want to Learn More? nataraj@autonomiq.io Confidential - Do Not Distribute 169/19/2018

×