SlideShare ist ein Scribd-Unternehmen logo
1 von 34
Agile Estimation & Planning
                 GrapeCity Inc               Apr 12, 2012




© 2011 GrapeCity inc.
What is an estimate ?


                              Unbiased, analytical process to
                              predict the duration or cost of a
                              project




      © 2011 GrapeCity inc.                                       2
By definition estimate is not accurate




      © 2011 GrapeCity inc.              3
Cone of Uncertainty




     © 2011 GrapeCity inc.   4
Cone of Uncertainty




     © 2011 GrapeCity inc.   5
Story Points




      © 2011 GrapeCity inc.   6
Story Points
•   Probably the most commonly used estimating unit
    among agile teams today
    – Name is derived from agile teams commonly expressing
      requirements as “user stories”
•   Based on combination of the size , unknowns and
    complexity of the work
•   Unit less but numerically relevant estimates
    – A 10 – point user story is expected to take twice as long
      as a 5 – point user story




          © 2011 GrapeCity inc.                                   7
We are good in comparing things




     © 2011 GrapeCity inc.        8
Time is not persistent




      © 2011 GrapeCity inc.   9
Three key advantages
•   Estimating in story points:
    – Forces the use of relative estimating . Studies have
      shown we are better at this
•   Focuses us on estimating the size , not the duration
    – We derive duration empirically by seeing how much we
      complete per iteration
•   Puts estimates in units that we can add together
    – Time based estimates are not additive




          © 2011 GrapeCity inc.                              10
Zoo points
 What value in “zoo
 points” would you put         Lion
 on these zoo                  Kangaroo
 animals?                      Rhinoceros
                               Bear
                               Giraffe
                               Gorilla
                               Hippopotamus
                               Tiger

       © 2011 GrapeCity inc.                  11
Estimating in Trees, Probing the Unknown




       © 2011 GrapeCity inc.               12
Estimating in Trees, Probing the Unknown




       © 2011 GrapeCity inc.               13
Estimating in Trees, Probing the Unknown




       © 2011 GrapeCity inc.               14
Estimating in Trees, Probing the Unknown




       © 2011 GrapeCity inc.               15
Estimating in Trees, Probing the Unknown




       © 2011 GrapeCity inc.               16
Planning Poker for estimating
An iterative approach to estimating
Steps
  • Each estimator is given a deck of cards, each card
    has a valid estimate written on it
  • Customer/Product owner reads a story and it’s
    discussed briefly
  • Each estimator selects a card that’s his or her
    estimate
  • Cards are turned over so all can see them
  • Discuss differences (especially outliers)
  • Re-estimate until estimates converge


        © 2011 GrapeCity inc.                            17
Estimating without Planning Poker
Product Owner                   Team starts thinking about how long
                                the story will take (in ideal man-days)




        © 2011 GrapeCity inc.                                             18
Estimating without Planning Poker




  Mr A believes that he knows exactly what needs to be
  done, so he thinks this will take 3 days. Mr B and C are
  more pessimistic. Mr D and E are slacking off. So Mr A
  says "3 days".

        © 2011 GrapeCity inc.                                19
Estimating without Planning Poker




  This makes B and C confused. They start doubting their
  own estimates. As you can see, the rest of the team
  has been heavily influenced by A, just because A spoke
  up first.

       © 2011 GrapeCity inc.                               20
Estimating with Planning Poker
Imagine that each team member is holding a deck of cards,
containing the following cards:



Product Owner                    Once again, the team starts thinking
                                 about how long the story will take.




         © 2011 GrapeCity inc.                                          21
Estimating with Planning Poker




 This time nobody blurts anything out. Instead they all have to
 present a card, face down, containing their estimate. When they
 are done, all cards are turned over simultaneously, revealing
 everyone's estimates

         © 2011 GrapeCity inc.                                     22
Estimating with Planning Poker




Whoops! Big divergence here. The team, in particular Mr A and
Mr C, need to discuss this story and why their estimates are so
wildly different. After some discussion, Mr A realizes that he has
forgotten some important tasks that need to be included in the
story. Mr C realizes that, with the design that Mr A presented,
the story might be smaller than 20.

         © 2011 GrapeCity inc.                                       23
Why Planning Poker works
•   Those who will do the work ,estimate the work.
•   Estimators are required to justify estimates
•   Combining of individual estimates through group
    discussion leads to better estimates.
•   Emphasizes relative rather than absolute
    estimating
•   Estimates are constrained to a set of values so we
    don’t waste time in meaningless arguments
•   Everyone’s opinion is heard.


         © 2011 GrapeCity inc.                           24
Release Planning




     © 2011 GrapeCity inc.   25
© 2011 GrapeCity inc.   26
Release Planning Meeting
                             Release Planning Meeting




                      Release Plan
                       Sprint 1 Sprint 2     Sprint 3   Sprints 4−7




     © 2011 GrapeCity inc.                                            27
An example with velocity = 14
                                                        Story A Story F
Sprint 1                                       Sprint 3−4
                                                        5        3
 Story A                         Story B        Story H        Story J
 5                               8              13      Story B Story G
                                                       Story I 8
         Story E                                        8        3
                                                       5         Story H
         1
                                                   Story C       13
                                                   3
Sprint 2                                                         Story I
                              Story F              Story D       5
Story C
                              3                    5
3                                                                Story J
                                     Story G           Story E
     Story D
                                                                 8
                                     3                 1
     5       © 2011 GrapeCity inc.                                         28
Projections based on velocity

40
                               Mean (Best 3) = 37
                               Mean (Last 8) = 33
30
                               Mean (Worst 3) = 28
20

10

 0
     1 2 3 4 5 6 7 8 9


       © 2011 GrapeCity inc.                         29
Extrapolate from velocity
                                            Assume 5
                                            sprints left


                               At our slowest velocity,we’ll end here (5 × 28)

                              At our average velocity,we’ll end here (5 × 33)

                              At our best velocity, we’ll end here (5 × 37)




      © 2011 GrapeCity inc.                                                      30
Burndown charts




     © 2011 GrapeCity inc.   31
An iteration burndown chart




      © 2011 GrapeCity inc.   32
A release burndown chart




     © 2011 GrapeCity inc.   33
Thank You



© 2011 GrapeCity inc.               34

Weitere ähnliche Inhalte

Andere mochten auch

Webinar on Agile Estimation : iZenBridge
Webinar on Agile Estimation : iZenBridgeWebinar on Agile Estimation : iZenBridge
Webinar on Agile Estimation : iZenBridgeSaket Bansal
 
User story estimation with agile architectures
User story estimation with agile architecturesUser story estimation with agile architectures
User story estimation with agile architecturesRaffaele Garofalo
 
Agile webinar بالعربي Planning ,estimation and story points
Agile webinar بالعربي Planning ,estimation and story pointsAgile webinar بالعربي Planning ,estimation and story points
Agile webinar بالعربي Planning ,estimation and story pointsAgile Arena
 
Agile Estimating
Agile EstimatingAgile Estimating
Agile EstimatingMike Cohn
 
[HCM Scrum Breakfast] Agile estimation - Story points
[HCM Scrum Breakfast] Agile estimation - Story points[HCM Scrum Breakfast] Agile estimation - Story points
[HCM Scrum Breakfast] Agile estimation - Story pointsScrum Breakfast Vietnam
 
Release planning in Scrum
Release planning in ScrumRelease planning in Scrum
Release planning in ScrumArne Åhlander
 
Agile Estimation for Fixed Price Model
Agile Estimation for Fixed Price ModelAgile Estimation for Fixed Price Model
Agile Estimation for Fixed Price Modeljayanth72
 
Agile Estimating & Planning by Amaad Qureshi
Agile Estimating & Planning by Amaad QureshiAgile Estimating & Planning by Amaad Qureshi
Agile Estimating & Planning by Amaad QureshiAmaad Qureshi
 
Introduction to Agile Estimation & Planning
Introduction to Agile Estimation & PlanningIntroduction to Agile Estimation & Planning
Introduction to Agile Estimation & PlanningAmaad Qureshi
 
AgileChina 2015: Agile Estimation Workshop
AgileChina 2015: Agile Estimation WorkshopAgileChina 2015: Agile Estimation Workshop
AgileChina 2015: Agile Estimation WorkshopStephen Vance
 
Agile Estimating and Planning Using Scrum
Agile Estimating and Planning Using ScrumAgile Estimating and Planning Using Scrum
Agile Estimating and Planning Using ScrumTommy Norman
 
Agile Release Planning
Agile Release PlanningAgile Release Planning
Agile Release PlanningAdnan Aziz
 
Understanding the Agile Release and Sprint Planning Process
Understanding the Agile Release and Sprint Planning Process Understanding the Agile Release and Sprint Planning Process
Understanding the Agile Release and Sprint Planning Process John Derrico
 
Introduction to Agile software testing
Introduction to Agile software testingIntroduction to Agile software testing
Introduction to Agile software testingKMS Technology
 

Andere mochten auch (14)

Webinar on Agile Estimation : iZenBridge
Webinar on Agile Estimation : iZenBridgeWebinar on Agile Estimation : iZenBridge
Webinar on Agile Estimation : iZenBridge
 
User story estimation with agile architectures
User story estimation with agile architecturesUser story estimation with agile architectures
User story estimation with agile architectures
 
Agile webinar بالعربي Planning ,estimation and story points
Agile webinar بالعربي Planning ,estimation and story pointsAgile webinar بالعربي Planning ,estimation and story points
Agile webinar بالعربي Planning ,estimation and story points
 
Agile Estimating
Agile EstimatingAgile Estimating
Agile Estimating
 
[HCM Scrum Breakfast] Agile estimation - Story points
[HCM Scrum Breakfast] Agile estimation - Story points[HCM Scrum Breakfast] Agile estimation - Story points
[HCM Scrum Breakfast] Agile estimation - Story points
 
Release planning in Scrum
Release planning in ScrumRelease planning in Scrum
Release planning in Scrum
 
Agile Estimation for Fixed Price Model
Agile Estimation for Fixed Price ModelAgile Estimation for Fixed Price Model
Agile Estimation for Fixed Price Model
 
Agile Estimating & Planning by Amaad Qureshi
Agile Estimating & Planning by Amaad QureshiAgile Estimating & Planning by Amaad Qureshi
Agile Estimating & Planning by Amaad Qureshi
 
Introduction to Agile Estimation & Planning
Introduction to Agile Estimation & PlanningIntroduction to Agile Estimation & Planning
Introduction to Agile Estimation & Planning
 
AgileChina 2015: Agile Estimation Workshop
AgileChina 2015: Agile Estimation WorkshopAgileChina 2015: Agile Estimation Workshop
AgileChina 2015: Agile Estimation Workshop
 
Agile Estimating and Planning Using Scrum
Agile Estimating and Planning Using ScrumAgile Estimating and Planning Using Scrum
Agile Estimating and Planning Using Scrum
 
Agile Release Planning
Agile Release PlanningAgile Release Planning
Agile Release Planning
 
Understanding the Agile Release and Sprint Planning Process
Understanding the Agile Release and Sprint Planning Process Understanding the Agile Release and Sprint Planning Process
Understanding the Agile Release and Sprint Planning Process
 
Introduction to Agile software testing
Introduction to Agile software testingIntroduction to Agile software testing
Introduction to Agile software testing
 

Kürzlich hochgeladen

Data Cloud, More than a CDP by Matt Robison
Data Cloud, More than a CDP by Matt RobisonData Cloud, More than a CDP by Matt Robison
Data Cloud, More than a CDP by Matt RobisonAnna Loughnan Colquhoun
 
Automating Google Workspace (GWS) & more with Apps Script
Automating Google Workspace (GWS) & more with Apps ScriptAutomating Google Workspace (GWS) & more with Apps Script
Automating Google Workspace (GWS) & more with Apps Scriptwesley chun
 
Raspberry Pi 5: Challenges and Solutions in Bringing up an OpenGL/Vulkan Driv...
Raspberry Pi 5: Challenges and Solutions in Bringing up an OpenGL/Vulkan Driv...Raspberry Pi 5: Challenges and Solutions in Bringing up an OpenGL/Vulkan Driv...
Raspberry Pi 5: Challenges and Solutions in Bringing up an OpenGL/Vulkan Driv...Igalia
 
IAC 2024 - IA Fast Track to Search Focused AI Solutions
IAC 2024 - IA Fast Track to Search Focused AI SolutionsIAC 2024 - IA Fast Track to Search Focused AI Solutions
IAC 2024 - IA Fast Track to Search Focused AI SolutionsEnterprise Knowledge
 
TrustArc Webinar - Stay Ahead of US State Data Privacy Law Developments
TrustArc Webinar - Stay Ahead of US State Data Privacy Law DevelopmentsTrustArc Webinar - Stay Ahead of US State Data Privacy Law Developments
TrustArc Webinar - Stay Ahead of US State Data Privacy Law DevelopmentsTrustArc
 
Boost Fertility New Invention Ups Success Rates.pdf
Boost Fertility New Invention Ups Success Rates.pdfBoost Fertility New Invention Ups Success Rates.pdf
Boost Fertility New Invention Ups Success Rates.pdfsudhanshuwaghmare1
 
EIS-Webinar-Prompt-Knowledge-Eng-2024-04-08.pptx
EIS-Webinar-Prompt-Knowledge-Eng-2024-04-08.pptxEIS-Webinar-Prompt-Knowledge-Eng-2024-04-08.pptx
EIS-Webinar-Prompt-Knowledge-Eng-2024-04-08.pptxEarley Information Science
 
🐬 The future of MySQL is Postgres 🐘
🐬  The future of MySQL is Postgres   🐘🐬  The future of MySQL is Postgres   🐘
🐬 The future of MySQL is Postgres 🐘RTylerCroy
 
A Call to Action for Generative AI in 2024
A Call to Action for Generative AI in 2024A Call to Action for Generative AI in 2024
A Call to Action for Generative AI in 2024Results
 
GenCyber Cyber Security Day Presentation
GenCyber Cyber Security Day PresentationGenCyber Cyber Security Day Presentation
GenCyber Cyber Security Day PresentationMichael W. Hawkins
 
Bajaj Allianz Life Insurance Company - Insurer Innovation Award 2024
Bajaj Allianz Life Insurance Company - Insurer Innovation Award 2024Bajaj Allianz Life Insurance Company - Insurer Innovation Award 2024
Bajaj Allianz Life Insurance Company - Insurer Innovation Award 2024The Digital Insurer
 
Apidays Singapore 2024 - Building Digital Trust in a Digital Economy by Veron...
Apidays Singapore 2024 - Building Digital Trust in a Digital Economy by Veron...Apidays Singapore 2024 - Building Digital Trust in a Digital Economy by Veron...
Apidays Singapore 2024 - Building Digital Trust in a Digital Economy by Veron...apidays
 
Real Time Object Detection Using Open CV
Real Time Object Detection Using Open CVReal Time Object Detection Using Open CV
Real Time Object Detection Using Open CVKhem
 
2024: Domino Containers - The Next Step. News from the Domino Container commu...
2024: Domino Containers - The Next Step. News from the Domino Container commu...2024: Domino Containers - The Next Step. News from the Domino Container commu...
2024: Domino Containers - The Next Step. News from the Domino Container commu...Martijn de Jong
 
Mastering MySQL Database Architecture: Deep Dive into MySQL Shell and MySQL R...
Mastering MySQL Database Architecture: Deep Dive into MySQL Shell and MySQL R...Mastering MySQL Database Architecture: Deep Dive into MySQL Shell and MySQL R...
Mastering MySQL Database Architecture: Deep Dive into MySQL Shell and MySQL R...Miguel Araújo
 
The 7 Things I Know About Cyber Security After 25 Years | April 2024
The 7 Things I Know About Cyber Security After 25 Years | April 2024The 7 Things I Know About Cyber Security After 25 Years | April 2024
The 7 Things I Know About Cyber Security After 25 Years | April 2024Rafal Los
 
A Domino Admins Adventures (Engage 2024)
A Domino Admins Adventures (Engage 2024)A Domino Admins Adventures (Engage 2024)
A Domino Admins Adventures (Engage 2024)Gabriella Davis
 
Artificial Intelligence: Facts and Myths
Artificial Intelligence: Facts and MythsArtificial Intelligence: Facts and Myths
Artificial Intelligence: Facts and MythsJoaquim Jorge
 
CNv6 Instructor Chapter 6 Quality of Service
CNv6 Instructor Chapter 6 Quality of ServiceCNv6 Instructor Chapter 6 Quality of Service
CNv6 Instructor Chapter 6 Quality of Servicegiselly40
 
08448380779 Call Girls In Friends Colony Women Seeking Men
08448380779 Call Girls In Friends Colony Women Seeking Men08448380779 Call Girls In Friends Colony Women Seeking Men
08448380779 Call Girls In Friends Colony Women Seeking MenDelhi Call girls
 

Kürzlich hochgeladen (20)

Data Cloud, More than a CDP by Matt Robison
Data Cloud, More than a CDP by Matt RobisonData Cloud, More than a CDP by Matt Robison
Data Cloud, More than a CDP by Matt Robison
 
Automating Google Workspace (GWS) & more with Apps Script
Automating Google Workspace (GWS) & more with Apps ScriptAutomating Google Workspace (GWS) & more with Apps Script
Automating Google Workspace (GWS) & more with Apps Script
 
Raspberry Pi 5: Challenges and Solutions in Bringing up an OpenGL/Vulkan Driv...
Raspberry Pi 5: Challenges and Solutions in Bringing up an OpenGL/Vulkan Driv...Raspberry Pi 5: Challenges and Solutions in Bringing up an OpenGL/Vulkan Driv...
Raspberry Pi 5: Challenges and Solutions in Bringing up an OpenGL/Vulkan Driv...
 
IAC 2024 - IA Fast Track to Search Focused AI Solutions
IAC 2024 - IA Fast Track to Search Focused AI SolutionsIAC 2024 - IA Fast Track to Search Focused AI Solutions
IAC 2024 - IA Fast Track to Search Focused AI Solutions
 
TrustArc Webinar - Stay Ahead of US State Data Privacy Law Developments
TrustArc Webinar - Stay Ahead of US State Data Privacy Law DevelopmentsTrustArc Webinar - Stay Ahead of US State Data Privacy Law Developments
TrustArc Webinar - Stay Ahead of US State Data Privacy Law Developments
 
Boost Fertility New Invention Ups Success Rates.pdf
Boost Fertility New Invention Ups Success Rates.pdfBoost Fertility New Invention Ups Success Rates.pdf
Boost Fertility New Invention Ups Success Rates.pdf
 
EIS-Webinar-Prompt-Knowledge-Eng-2024-04-08.pptx
EIS-Webinar-Prompt-Knowledge-Eng-2024-04-08.pptxEIS-Webinar-Prompt-Knowledge-Eng-2024-04-08.pptx
EIS-Webinar-Prompt-Knowledge-Eng-2024-04-08.pptx
 
🐬 The future of MySQL is Postgres 🐘
🐬  The future of MySQL is Postgres   🐘🐬  The future of MySQL is Postgres   🐘
🐬 The future of MySQL is Postgres 🐘
 
A Call to Action for Generative AI in 2024
A Call to Action for Generative AI in 2024A Call to Action for Generative AI in 2024
A Call to Action for Generative AI in 2024
 
GenCyber Cyber Security Day Presentation
GenCyber Cyber Security Day PresentationGenCyber Cyber Security Day Presentation
GenCyber Cyber Security Day Presentation
 
Bajaj Allianz Life Insurance Company - Insurer Innovation Award 2024
Bajaj Allianz Life Insurance Company - Insurer Innovation Award 2024Bajaj Allianz Life Insurance Company - Insurer Innovation Award 2024
Bajaj Allianz Life Insurance Company - Insurer Innovation Award 2024
 
Apidays Singapore 2024 - Building Digital Trust in a Digital Economy by Veron...
Apidays Singapore 2024 - Building Digital Trust in a Digital Economy by Veron...Apidays Singapore 2024 - Building Digital Trust in a Digital Economy by Veron...
Apidays Singapore 2024 - Building Digital Trust in a Digital Economy by Veron...
 
Real Time Object Detection Using Open CV
Real Time Object Detection Using Open CVReal Time Object Detection Using Open CV
Real Time Object Detection Using Open CV
 
2024: Domino Containers - The Next Step. News from the Domino Container commu...
2024: Domino Containers - The Next Step. News from the Domino Container commu...2024: Domino Containers - The Next Step. News from the Domino Container commu...
2024: Domino Containers - The Next Step. News from the Domino Container commu...
 
Mastering MySQL Database Architecture: Deep Dive into MySQL Shell and MySQL R...
Mastering MySQL Database Architecture: Deep Dive into MySQL Shell and MySQL R...Mastering MySQL Database Architecture: Deep Dive into MySQL Shell and MySQL R...
Mastering MySQL Database Architecture: Deep Dive into MySQL Shell and MySQL R...
 
The 7 Things I Know About Cyber Security After 25 Years | April 2024
The 7 Things I Know About Cyber Security After 25 Years | April 2024The 7 Things I Know About Cyber Security After 25 Years | April 2024
The 7 Things I Know About Cyber Security After 25 Years | April 2024
 
A Domino Admins Adventures (Engage 2024)
A Domino Admins Adventures (Engage 2024)A Domino Admins Adventures (Engage 2024)
A Domino Admins Adventures (Engage 2024)
 
Artificial Intelligence: Facts and Myths
Artificial Intelligence: Facts and MythsArtificial Intelligence: Facts and Myths
Artificial Intelligence: Facts and Myths
 
CNv6 Instructor Chapter 6 Quality of Service
CNv6 Instructor Chapter 6 Quality of ServiceCNv6 Instructor Chapter 6 Quality of Service
CNv6 Instructor Chapter 6 Quality of Service
 
08448380779 Call Girls In Friends Colony Women Seeking Men
08448380779 Call Girls In Friends Colony Women Seeking Men08448380779 Call Girls In Friends Colony Women Seeking Men
08448380779 Call Girls In Friends Colony Women Seeking Men
 

Agile Estimation & Planning Techniques

  • 1. Agile Estimation & Planning GrapeCity Inc Apr 12, 2012 © 2011 GrapeCity inc.
  • 2. What is an estimate ? Unbiased, analytical process to predict the duration or cost of a project © 2011 GrapeCity inc. 2
  • 3. By definition estimate is not accurate © 2011 GrapeCity inc. 3
  • 4. Cone of Uncertainty © 2011 GrapeCity inc. 4
  • 5. Cone of Uncertainty © 2011 GrapeCity inc. 5
  • 6. Story Points © 2011 GrapeCity inc. 6
  • 7. Story Points • Probably the most commonly used estimating unit among agile teams today – Name is derived from agile teams commonly expressing requirements as “user stories” • Based on combination of the size , unknowns and complexity of the work • Unit less but numerically relevant estimates – A 10 – point user story is expected to take twice as long as a 5 – point user story © 2011 GrapeCity inc. 7
  • 8. We are good in comparing things © 2011 GrapeCity inc. 8
  • 9. Time is not persistent © 2011 GrapeCity inc. 9
  • 10. Three key advantages • Estimating in story points: – Forces the use of relative estimating . Studies have shown we are better at this • Focuses us on estimating the size , not the duration – We derive duration empirically by seeing how much we complete per iteration • Puts estimates in units that we can add together – Time based estimates are not additive © 2011 GrapeCity inc. 10
  • 11. Zoo points What value in “zoo points” would you put Lion on these zoo Kangaroo animals? Rhinoceros Bear Giraffe Gorilla Hippopotamus Tiger © 2011 GrapeCity inc. 11
  • 12. Estimating in Trees, Probing the Unknown © 2011 GrapeCity inc. 12
  • 13. Estimating in Trees, Probing the Unknown © 2011 GrapeCity inc. 13
  • 14. Estimating in Trees, Probing the Unknown © 2011 GrapeCity inc. 14
  • 15. Estimating in Trees, Probing the Unknown © 2011 GrapeCity inc. 15
  • 16. Estimating in Trees, Probing the Unknown © 2011 GrapeCity inc. 16
  • 17. Planning Poker for estimating An iterative approach to estimating Steps • Each estimator is given a deck of cards, each card has a valid estimate written on it • Customer/Product owner reads a story and it’s discussed briefly • Each estimator selects a card that’s his or her estimate • Cards are turned over so all can see them • Discuss differences (especially outliers) • Re-estimate until estimates converge © 2011 GrapeCity inc. 17
  • 18. Estimating without Planning Poker Product Owner Team starts thinking about how long the story will take (in ideal man-days) © 2011 GrapeCity inc. 18
  • 19. Estimating without Planning Poker Mr A believes that he knows exactly what needs to be done, so he thinks this will take 3 days. Mr B and C are more pessimistic. Mr D and E are slacking off. So Mr A says "3 days". © 2011 GrapeCity inc. 19
  • 20. Estimating without Planning Poker This makes B and C confused. They start doubting their own estimates. As you can see, the rest of the team has been heavily influenced by A, just because A spoke up first. © 2011 GrapeCity inc. 20
  • 21. Estimating with Planning Poker Imagine that each team member is holding a deck of cards, containing the following cards: Product Owner Once again, the team starts thinking about how long the story will take. © 2011 GrapeCity inc. 21
  • 22. Estimating with Planning Poker This time nobody blurts anything out. Instead they all have to present a card, face down, containing their estimate. When they are done, all cards are turned over simultaneously, revealing everyone's estimates © 2011 GrapeCity inc. 22
  • 23. Estimating with Planning Poker Whoops! Big divergence here. The team, in particular Mr A and Mr C, need to discuss this story and why their estimates are so wildly different. After some discussion, Mr A realizes that he has forgotten some important tasks that need to be included in the story. Mr C realizes that, with the design that Mr A presented, the story might be smaller than 20. © 2011 GrapeCity inc. 23
  • 24. Why Planning Poker works • Those who will do the work ,estimate the work. • Estimators are required to justify estimates • Combining of individual estimates through group discussion leads to better estimates. • Emphasizes relative rather than absolute estimating • Estimates are constrained to a set of values so we don’t waste time in meaningless arguments • Everyone’s opinion is heard. © 2011 GrapeCity inc. 24
  • 25. Release Planning © 2011 GrapeCity inc. 25
  • 26. © 2011 GrapeCity inc. 26
  • 27. Release Planning Meeting Release Planning Meeting Release Plan Sprint 1 Sprint 2 Sprint 3 Sprints 4−7 © 2011 GrapeCity inc. 27
  • 28. An example with velocity = 14 Story A Story F Sprint 1 Sprint 3−4 5 3 Story A Story B Story H Story J 5 8 13 Story B Story G Story I 8 Story E 8 3 5 Story H 1 Story C 13 3 Sprint 2 Story I Story F Story D 5 Story C 3 5 3 Story J Story G Story E Story D 8 3 1 5 © 2011 GrapeCity inc. 28
  • 29. Projections based on velocity 40 Mean (Best 3) = 37 Mean (Last 8) = 33 30 Mean (Worst 3) = 28 20 10 0 1 2 3 4 5 6 7 8 9 © 2011 GrapeCity inc. 29
  • 30. Extrapolate from velocity Assume 5 sprints left At our slowest velocity,we’ll end here (5 × 28) At our average velocity,we’ll end here (5 × 33) At our best velocity, we’ll end here (5 × 37) © 2011 GrapeCity inc. 30
  • 31. Burndown charts © 2011 GrapeCity inc. 31
  • 32. An iteration burndown chart © 2011 GrapeCity inc. 32
  • 33. A release burndown chart © 2011 GrapeCity inc. 33
  • 34. Thank You © 2011 GrapeCity inc. 34

Hinweis der Redaktion

  1. The vertical axis contains the degree of error that has been found in estimates created by skilled estimators at various points in the project. Estimates created very early in the project are subject to a high degree of error. Estimates created at Initial Concept time can be inaccurate by a factor of 4x on the high side or 4x on the low side (also expressed as 0.25x, which is just 1 divided by 4). The total range from high estimate to low estimate is 4x divided by 0.25x, or 16x.
  2. http://zuulcat.com/tag/agile/
  3. http://zuulcat.com/tag/agile/
  4. http://zuulcat.com/tag/agile/
  5. http://zuulcat.com/tag/agile/
  6. http://www.crisp.se/planningpoker
  7. http://www.crisp.se/planningpoker
  8. They thought they has 780 hrs of work . It went up as we figured out something's are more tough and discovered some new stuff . Team discovered they cant deliver on time . The product owner dropped some portions of it
  9. On this burndown chart, the team started a project that was planned to be eleven two-week sprints. They began with 200 story points of work. The first sprint went well and from the chart you can infer that they had around 180 story points of work remaining after the first sprint. During the second sprint, however, the estimated work remaining actually burned up. This could have been because work was added to the project or because the team changed some estimates of the remaining work. From there the project continued well. Progress slowed during sprint 7 but then quickly resumed.