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Tuning our Agile Process Using Systems Engineering

Tuning our Agile Process Using Systems Engineering. Mike Day mday@pingidentity.com Director of Software Quality January 27, 2011. Background Undergraduate Degrees in Business Administration, Finance, Economics Industries include Manufacturing, Non-Profit, Health Care, Telco

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Tuning our Agile Process Using Systems Engineering

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  1. Tuning our Agile ProcessUsing Systems Engineering Mike Day mday@pingidentity.com Director of Software Quality January 27, 2011

  2. Background • Undergraduate Degrees in Business Administration, Finance, Economics • Industries include Manufacturing, Non-Profit, Health Care, Telco • Masters Degree in Information Systems • Industries include Telco, Open Source, Enterprise and SaaS development • Practicing Agile for 10+ years • Consulting/Agile Coaching - late 90s/early 2000s • Ping Identity Agile Quality Team development • Passions • Systems Dynamics, Data Mining and Modeling, and the Growth and Leadership of Highly Performant Teams Who Am I…

  3. THE leader in Secure Internet Single Sign-On • PingFederate – On-Premise, Enterprise SSO solution • PingConnect – On-Demand SSO solution for SaaS and the Cloud • ~ 600 Customers Worldwide – growing at 1+ per day • > 40% of Fortune 100 Companies • 100+ Global Partners • Founded in 2002 – Privately Owned • Strong Cash position – Debt Free • Consistently Meeting or Exceeding Financial Goals • Headquartered in Denver • Offices in Boston, Istanbul, London, Moscow & Vancouver • Worldwide sales and services Who is Ping Identity

  4. Systems Engineering an interdisciplinary field of engineering that focuses on how complex engineering projects should be designed and managed over the life cycle of the project. Issues such as logistics, the coordination of different teams, and automatic control of machinery become more difficult when dealing with large, complex projects. Systems engineering deals with work-processes and tools to handle such projects, and it overlaps with both technical and human-centered disciplines such as control engineering, industrial engineering, organizational studies, and project management. System Dynamics an approach to understanding the behaviour of complex systems over time. It deals with internal feedback loops and time delays that affect the behaviour of the entire system Define:Systems Engineering

  5. Iterate Iterate Iterate Iterate Iterate Iterate Iterate Iterate Iterate Iterate Iterate Iterate Iterate Iterate Iterate Iterate Iterate Iterate Features Features Features Features Customers V V 2 Product 1 Agile @ Ping Planning Queue Product Owners Define Plan Accept? ? ? Releases System Releases Inception Transition Elaboration and Construction Hardened Features Integrated Product Rollout • Business analysis thinking-throughout • Requirements Elaboration throughout • Requirements Matrix-built progressively • Supplemental Specification • In-Flight Agile Dashboard drives feature priority throughout

  6. Agile From Initial Dev Team Staffing  7 Years • Distributed Agile: Teams/Individuals in 4 Countries • Fixed Timeboxes  < 3% slips  ~12 days/7 years Agile @ Ping • 2006: 26 Releases • 2007: 28 Releases • 2008: 22 Releases • 2009: 40 Releases • 2010: 39 Releases • & Low Field Defects (94% DDP) Flywheel Expectations

  7. Management and Mitigation • Quality Debt • Risk • Team Development and Dynamics • Velocity • Delivery to a time-boxed cadence • Commitment to Quality Agile Pain Points… Throughout the Years Systems Engineering

  8. Key Drivers for Analysis • Delays in Addressing Defects • Feature Development vs. Defect Correction • Teams are Different • Minimum Quality Criteria • Data Mining and Modeling Addressing Concerns • Engineering Dashboards • Quality Debt Paintball • Reliability Growth Modeling • Defect Lifetime Analysis • Defect Detection Percentage Quality Debt Management

  9. Engineering Dashboard

  10. Quality Debt Paintball

  11. Reliability Growth Modeling

  12. Defect Lifetime Analysis 82%

  13. 162 Total Field Escapes since July 2007 Defect Detection Percentage

  14. Key Drivers for Analysis • Extremely Feature Rich Product Offering • Execution of More with Less • Teams are Different • Minimum Quality Criteria • Data Mining and Modeling Addressing Concerns • Understanding the Product Longtail • Risk Mitigation Modeling • Code Change TreeMaps Risk Management and Mitigation

  15. Understanding the Product Long tail

  16. Risk Mitigation Modeling

  17. Risk Mitigation Modeling (Cont.)

  18. Code Change TreeMaps

  19. Key Drivers for Analysis • Enterprise Software Development != SaaS Development • Executing more Frequently • Teams are Different • Minimum Quality Criteria • Data Mining and Modeling Addressing Concerns • Engineering Dashboard • Complexity Point Modeling Understanding and Improving Team Velocity

  20. Engineering Dashboard

  21. Complexity Point Modeling

  22. Complexity Point Modeling

  23. Analysis of Adoption of Product Offerings • Frequency • Development to First Adoption Temporal Map • Understanding Cost/Benefit • Modeling ROI • Defining Level of Effort on Incremental Work • Test Asset Management • Consolidation and Reporting of all Automated Tests • Bringing All Tests under Single Management Umbrella • Monitoring Test Effectiveness and Coverage What’s Next… Systems Engineering @ Ping

  24. Wrap it Up…

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