SlideShare a Scribd company logo
Dr. Mohammed Taha Abulhaija
© mabulhaija
© All Rights Reserved: The contents of all material available on these slides are copyrighted. You
can always use this content or a part of it by referencing the author name.
Sprint Planning
Update
Backlog
Select & Refine
User Stories
Commit to
Sprint Backlog
1 : 9
Duration
Sprint Planning should not exceeds half a day of two-week sprint
© mabulhaija
Which Backlog User Story to include in the Sprint Backlog
Story
Story
Story
Sprint Planning
© mabulhaija
Great User Story Writing
Planning Game: Poker
The key to making a good Sprint Planning Work is two-fold
Sprint Planning
© mabulhaija
Loudest voice in the room wins
There's no timebox or time limit on discussions
There's no way to tell when consensus is reached
There's no way to improve without structure to
improve upon
Sprint Planning should not exceeds half a day of two-week sprint
Sprint Planning
© mabulhaija
Practical Agile
The Race – Knowing The Effort
Target
Training
Efficiency Test
KPI
Result
Why
• 400m Race in 1:13 minute by
maintaining a Sustainable Pace
• Daily for one year
• Every two weeks
• Time the whole team needs
to finish the 400m race
• Efficiency Increased
• Keep Training & Lessons
Learning
© mabulhaija
NOW WE
KNOW THAT
Our Team can
finish the
race in 1
minute & 13
seconds
1
1.25
1.5
1.75
2
2.25
2.5
2.75
3
1 3 5 7 9 11 13 15 17 19 21 23 25
Time
Each number represent two weeks
Time to finish 400m race
The Race
By daily training: The team improve his time through the year from
3 to 1:13 minutes. ONE CONDITION applied – Sustainable Pace
© mabulhaija
The Race – Another Point View
They have a FIXED PACE every day of training, How they
improve their time?
The team learns to:
 Work Together
 Trust & Support each other
 Be Self Organized: remember no TRAINER
 Review their Improvement every two weeks
and CHANGED according to what they LEARN
 Improved Frequently
 Sustainable Improvement: after 6 months they
reach SATISFACTION – an acceptable time
 Always focus on their Target
 They Enjoy their time and keep the training Simple
NOW WE
KNOW THAT
The team now
knows what
EFFORT they
should made to
finish the 400m
race in 1:13
minute, and
HOW they can
do that.
© mabulhaija
The Race – Another Point View
 What The Team Do?
They were being an AGILE Team. They Apply Agile 12
Principles to reach their target.
 What Was The Team Learning Result?
They knows what EFFORT they should made to reach their
target.
© mabulhaija
The Team knows
What they can do (Size of Work) in 01:13 minutes to
PASS 400M Race
The Race – Summary
© mabulhaija
Pass
400m RaceEffort
Effort
01:13
Minute
TheTeamTogether
01:13
Minute
01:13
Minute
Pass
400m Race
Pass
400m Race
Effort
ThisisWhattheteamneedstoknowto
PLANtheirSPRINTBACKLOG
The Race – Sustainable Pace
What is the Sustainable Pace?
 The Effort the team made Today
Should not affect their
performance Tomorrow.
 The team should BALANCE
their work, family and
social life.
i.e. is one at which the team can perform for very
long periods of time – forever basically.
 You can’t work all weekends in your life
 You can’t have overtime forever
 You can’t work tomorrow if you kill yourself today
Sustainable
Development
Improvement
Quality
Delivery
© Agile Gnostic
© Bob Hartman
© mabulhaija
Planning Poker
In Sprint Planning the team select stories from the Backlog to be in the
Sprint Backlog, which means these Stories can be done (Implemented &
Tested) within the Sprint Timebox, which mostly be Two Weeks and some
times Four Weeks.
© mabulhaija
Sprint Backlog
??
?
?
How the team knows
which Stories can be
done within sprint
Planning Poker
Facts
 The Team is an Agile Team – Applies Agile 12 Principles
 The Team works on Sustainable Pace = No Overtime, No Weekends
Work, . . .
 Two-Week = 10 Working Days, and the working time is 8 Hours. i.e. Two-
Week = 80 Working Hours.
© mabulhaija
 The team by Experience & Continuous Learning knows TOGETHER what
Size of Work they can do in Two Weeks (80 Hours). f.g. let’s say 27
The Team
Effort Value
27
Known by experience
Planning Poker – Definition
Planning Poker also called Scrum Poker, is a technique
for ESTIMATING, mostly used to estimate Effort or Size of
Work in Software Development.
In Agile it used to estimate the Effort needed for each
User Story in the Backlog.
Note That: Stories in the Backlog are sorted by Product
Owner based on its VALUE from the Customer point view.
You don’t need to do estimation for all stories, just do it for
stories you need in your Release.
© mabulhaija
Planning Poker – How
In Planning Poker we use Fibonacci Cards
It is a Playing Card of 6 sets, each set has its own color
and each set (Color) has a cards with values following the
Fibonacci Series (1, 2, 3, 5, 8, 13, 21) and the (?) card means
I’m Not Sure, also a (Coffee Break) card for when it all gets a
bit much!
© mabulhaija
Planning Poker – How
Now the team use the cards to estimate the EFFORT (NOT
Days or Hours) required by them as a Team Together, to
implement Each Story, following this procedure:
1. Team BRIEFLY discuss a story
2. Everyone SILENTLY select a point card
3. Team reveals ALL selected cards in unison
4. If OUTLIERS exists, BRIEFLY discuss and Re-Vote
© mabulhaija
Planning Poker – How
Now all User Stories has an Effort Value (Rate)
and The Team knows what their Effort Value for Two-week
Sprint. So, by basic Sum Calculations they choose which
stories to be in the Sprint Backlog
© mabulhaija
© Jeff Dalton
The Team
Effort Value
27
Known by experience
Planning Poker – Team Effort
Team Effort enhanced by Experience, so its Effort Value
also increased by Experience, until it reach its best.
© mabulhaija
15
56
21 27 32 40
Enjoy The Estimation Game
Good Luck!

More Related Content

Similar to Planning Poker

Measuring Performance - Quantifying the Work of a Scrum Master
Measuring Performance - Quantifying the Work of a Scrum MasterMeasuring Performance - Quantifying the Work of a Scrum Master
Measuring Performance - Quantifying the Work of a Scrum Master
Stephanie Gasche
 
Sprint planninng
Sprint planninngSprint planninng
Sprint planninng
Deepak Gururaja
 
test_flash
test_flashtest_flash
test_flash
guest87cf43
 
Dream team slide show
Dream team slide showDream team slide show
Dream team slide show
jporter50
 
Mission Impossible: Banking on a SAFe QuickStart
Mission Impossible: Banking on a SAFe QuickStartMission Impossible: Banking on a SAFe QuickStart
Mission Impossible: Banking on a SAFe QuickStart
Em Campbell-Pretty
 
How to make your retrospectives the heart of your agile proces
How to make your retrospectives the heart of your agile procesHow to make your retrospectives the heart of your agile proces
How to make your retrospectives the heart of your agile proces
Yves Hanoulle
 
Scrum master checklist
Scrum master checklistScrum master checklist
Scrum master checklist
Shaju Rasheed
 
Let's learn scrum
Let's learn scrumLet's learn scrum
Let's learn scrum
Tarun Singh
 
Resort brochure game
Resort brochure gameResort brochure game
Resort brochure game
Gerry Kirk
 
SCRUM Intro
SCRUM IntroSCRUM Intro
SCRUM Intro
Bermon Painter
 
Stop! Collaborate & Strategize: Part 4
Stop! Collaborate & Strategize: Part 4Stop! Collaborate & Strategize: Part 4
Stop! Collaborate & Strategize: Part 4
UXPA International
 
3. Armine - retrospective and grooming
3. Armine - retrospective and grooming3. Armine - retrospective and grooming
3. Armine - retrospective and grooming
Arevik Harutyunyan
 
full-stack agile - Scrum Basics
full-stack agile -  Scrum Basicsfull-stack agile -  Scrum Basics
full-stack agile - Scrum Basics
Ashley-Christian Hardy
 
Backlog Refinement 101 & 202
Backlog Refinement 101 & 202Backlog Refinement 101 & 202
Backlog Refinement 101 & 202
David Hanson
 
Scaling XP Practices
Scaling XP PracticesScaling XP Practices
Scaling XP Practices
Naresh Jain
 
Behind the scenes of retrospective workshop-goat16-november 21th-2016-hand-out
Behind the scenes of retrospective workshop-goat16-november 21th-2016-hand-outBehind the scenes of retrospective workshop-goat16-november 21th-2016-hand-out
Behind the scenes of retrospective workshop-goat16-november 21th-2016-hand-out
Jesus Mendez
 
:: Agile Scrum Methodology ::
:: Agile Scrum Methodology :::: Agile Scrum Methodology ::
:: Agile Scrum Methodology ::
Zubaida Tasmeen Eliza 🇧🇩
 
Coaching: Full-Time Results on a Part-Time Schedule
Coaching: Full-Time Results on a Part-Time ScheduleCoaching: Full-Time Results on a Part-Time Schedule
Coaching: Full-Time Results on a Part-Time Schedule
Nielsen-Kellerman
 
Agile2022 What parkrun has taught me 2022-07-18.pdf
Agile2022 What parkrun has taught me 2022-07-18.pdfAgile2022 What parkrun has taught me 2022-07-18.pdf
Agile2022 What parkrun has taught me 2022-07-18.pdf
Mia Horrigan
 
Succeed with Scrum - Part 1
Succeed with Scrum - Part 1Succeed with Scrum - Part 1
Succeed with Scrum - Part 1
Satisha K Venkataramaiah
 

Similar to Planning Poker (20)

Measuring Performance - Quantifying the Work of a Scrum Master
Measuring Performance - Quantifying the Work of a Scrum MasterMeasuring Performance - Quantifying the Work of a Scrum Master
Measuring Performance - Quantifying the Work of a Scrum Master
 
Sprint planninng
Sprint planninngSprint planninng
Sprint planninng
 
test_flash
test_flashtest_flash
test_flash
 
Dream team slide show
Dream team slide showDream team slide show
Dream team slide show
 
Mission Impossible: Banking on a SAFe QuickStart
Mission Impossible: Banking on a SAFe QuickStartMission Impossible: Banking on a SAFe QuickStart
Mission Impossible: Banking on a SAFe QuickStart
 
How to make your retrospectives the heart of your agile proces
How to make your retrospectives the heart of your agile procesHow to make your retrospectives the heart of your agile proces
How to make your retrospectives the heart of your agile proces
 
Scrum master checklist
Scrum master checklistScrum master checklist
Scrum master checklist
 
Let's learn scrum
Let's learn scrumLet's learn scrum
Let's learn scrum
 
Resort brochure game
Resort brochure gameResort brochure game
Resort brochure game
 
SCRUM Intro
SCRUM IntroSCRUM Intro
SCRUM Intro
 
Stop! Collaborate & Strategize: Part 4
Stop! Collaborate & Strategize: Part 4Stop! Collaborate & Strategize: Part 4
Stop! Collaborate & Strategize: Part 4
 
3. Armine - retrospective and grooming
3. Armine - retrospective and grooming3. Armine - retrospective and grooming
3. Armine - retrospective and grooming
 
full-stack agile - Scrum Basics
full-stack agile -  Scrum Basicsfull-stack agile -  Scrum Basics
full-stack agile - Scrum Basics
 
Backlog Refinement 101 & 202
Backlog Refinement 101 & 202Backlog Refinement 101 & 202
Backlog Refinement 101 & 202
 
Scaling XP Practices
Scaling XP PracticesScaling XP Practices
Scaling XP Practices
 
Behind the scenes of retrospective workshop-goat16-november 21th-2016-hand-out
Behind the scenes of retrospective workshop-goat16-november 21th-2016-hand-outBehind the scenes of retrospective workshop-goat16-november 21th-2016-hand-out
Behind the scenes of retrospective workshop-goat16-november 21th-2016-hand-out
 
:: Agile Scrum Methodology ::
:: Agile Scrum Methodology :::: Agile Scrum Methodology ::
:: Agile Scrum Methodology ::
 
Coaching: Full-Time Results on a Part-Time Schedule
Coaching: Full-Time Results on a Part-Time ScheduleCoaching: Full-Time Results on a Part-Time Schedule
Coaching: Full-Time Results on a Part-Time Schedule
 
Agile2022 What parkrun has taught me 2022-07-18.pdf
Agile2022 What parkrun has taught me 2022-07-18.pdfAgile2022 What parkrun has taught me 2022-07-18.pdf
Agile2022 What parkrun has taught me 2022-07-18.pdf
 
Succeed with Scrum - Part 1
Succeed with Scrum - Part 1Succeed with Scrum - Part 1
Succeed with Scrum - Part 1
 

Recently uploaded

Building Production Ready Search Pipelines with Spark and Milvus
Building Production Ready Search Pipelines with Spark and MilvusBuilding Production Ready Search Pipelines with Spark and Milvus
Building Production Ready Search Pipelines with Spark and Milvus
Zilliz
 
Video Streaming: Then, Now, and in the Future
Video Streaming: Then, Now, and in the FutureVideo Streaming: Then, Now, and in the Future
Video Streaming: Then, Now, and in the Future
Alpen-Adria-Universität
 
HCL Notes and Domino License Cost Reduction in the World of DLAU
HCL Notes and Domino License Cost Reduction in the World of DLAUHCL Notes and Domino License Cost Reduction in the World of DLAU
HCL Notes and Domino License Cost Reduction in the World of DLAU
panagenda
 
20240609 QFM020 Irresponsible AI Reading List May 2024
20240609 QFM020 Irresponsible AI Reading List May 202420240609 QFM020 Irresponsible AI Reading List May 2024
20240609 QFM020 Irresponsible AI Reading List May 2024
Matthew Sinclair
 
UI5 Controls simplified - UI5con2024 presentation
UI5 Controls simplified - UI5con2024 presentationUI5 Controls simplified - UI5con2024 presentation
UI5 Controls simplified - UI5con2024 presentation
Wouter Lemaire
 
Taking AI to the Next Level in Manufacturing.pdf
Taking AI to the Next Level in Manufacturing.pdfTaking AI to the Next Level in Manufacturing.pdf
Taking AI to the Next Level in Manufacturing.pdf
ssuserfac0301
 
Fueling AI with Great Data with Airbyte Webinar
Fueling AI with Great Data with Airbyte WebinarFueling AI with Great Data with Airbyte Webinar
Fueling AI with Great Data with Airbyte Webinar
Zilliz
 
Your One-Stop Shop for Python Success: Top 10 US Python Development Providers
Your One-Stop Shop for Python Success: Top 10 US Python Development ProvidersYour One-Stop Shop for Python Success: Top 10 US Python Development Providers
Your One-Stop Shop for Python Success: Top 10 US Python Development Providers
akankshawande
 
Choosing The Best AWS Service For Your Website + API.pptx
Choosing The Best AWS Service For Your Website + API.pptxChoosing The Best AWS Service For Your Website + API.pptx
Choosing The Best AWS Service For Your Website + API.pptx
Brandon Minnick, MBA
 
Infrastructure Challenges in Scaling RAG with Custom AI models
Infrastructure Challenges in Scaling RAG with Custom AI modelsInfrastructure Challenges in Scaling RAG with Custom AI models
Infrastructure Challenges in Scaling RAG with Custom AI models
Zilliz
 
AI-Powered Food Delivery Transforming App Development in Saudi Arabia.pdf
AI-Powered Food Delivery Transforming App Development in Saudi Arabia.pdfAI-Powered Food Delivery Transforming App Development in Saudi Arabia.pdf
AI-Powered Food Delivery Transforming App Development in Saudi Arabia.pdf
Techgropse Pvt.Ltd.
 
Microsoft - Power Platform_G.Aspiotis.pdf
Microsoft - Power Platform_G.Aspiotis.pdfMicrosoft - Power Platform_G.Aspiotis.pdf
Microsoft - Power Platform_G.Aspiotis.pdf
Uni Systems S.M.S.A.
 
Ocean lotus Threat actors project by John Sitima 2024 (1).pptx
Ocean lotus Threat actors project by John Sitima 2024 (1).pptxOcean lotus Threat actors project by John Sitima 2024 (1).pptx
Ocean lotus Threat actors project by John Sitima 2024 (1).pptx
SitimaJohn
 
Monitoring and Managing Anomaly Detection on OpenShift.pdf
Monitoring and Managing Anomaly Detection on OpenShift.pdfMonitoring and Managing Anomaly Detection on OpenShift.pdf
Monitoring and Managing Anomaly Detection on OpenShift.pdf
Tosin Akinosho
 
“Building and Scaling AI Applications with the Nx AI Manager,” a Presentation...
“Building and Scaling AI Applications with the Nx AI Manager,” a Presentation...“Building and Scaling AI Applications with the Nx AI Manager,” a Presentation...
“Building and Scaling AI Applications with the Nx AI Manager,” a Presentation...
Edge AI and Vision Alliance
 
CAKE: Sharing Slices of Confidential Data on Blockchain
CAKE: Sharing Slices of Confidential Data on BlockchainCAKE: Sharing Slices of Confidential Data on Blockchain
CAKE: Sharing Slices of Confidential Data on Blockchain
Claudio Di Ciccio
 
GenAI Pilot Implementation in the organizations
GenAI Pilot Implementation in the organizationsGenAI Pilot Implementation in the organizations
GenAI Pilot Implementation in the organizations
kumardaparthi1024
 
How to use Firebase Data Connect For Flutter
How to use Firebase Data Connect For FlutterHow to use Firebase Data Connect For Flutter
How to use Firebase Data Connect For Flutter
Daiki Mogmet Ito
 
Serial Arm Control in Real Time Presentation
Serial Arm Control in Real Time PresentationSerial Arm Control in Real Time Presentation
Serial Arm Control in Real Time Presentation
tolgahangng
 
Uni Systems Copilot event_05062024_C.Vlachos.pdf
Uni Systems Copilot event_05062024_C.Vlachos.pdfUni Systems Copilot event_05062024_C.Vlachos.pdf
Uni Systems Copilot event_05062024_C.Vlachos.pdf
Uni Systems S.M.S.A.
 

Recently uploaded (20)

Building Production Ready Search Pipelines with Spark and Milvus
Building Production Ready Search Pipelines with Spark and MilvusBuilding Production Ready Search Pipelines with Spark and Milvus
Building Production Ready Search Pipelines with Spark and Milvus
 
Video Streaming: Then, Now, and in the Future
Video Streaming: Then, Now, and in the FutureVideo Streaming: Then, Now, and in the Future
Video Streaming: Then, Now, and in the Future
 
HCL Notes and Domino License Cost Reduction in the World of DLAU
HCL Notes and Domino License Cost Reduction in the World of DLAUHCL Notes and Domino License Cost Reduction in the World of DLAU
HCL Notes and Domino License Cost Reduction in the World of DLAU
 
20240609 QFM020 Irresponsible AI Reading List May 2024
20240609 QFM020 Irresponsible AI Reading List May 202420240609 QFM020 Irresponsible AI Reading List May 2024
20240609 QFM020 Irresponsible AI Reading List May 2024
 
UI5 Controls simplified - UI5con2024 presentation
UI5 Controls simplified - UI5con2024 presentationUI5 Controls simplified - UI5con2024 presentation
UI5 Controls simplified - UI5con2024 presentation
 
Taking AI to the Next Level in Manufacturing.pdf
Taking AI to the Next Level in Manufacturing.pdfTaking AI to the Next Level in Manufacturing.pdf
Taking AI to the Next Level in Manufacturing.pdf
 
Fueling AI with Great Data with Airbyte Webinar
Fueling AI with Great Data with Airbyte WebinarFueling AI with Great Data with Airbyte Webinar
Fueling AI with Great Data with Airbyte Webinar
 
Your One-Stop Shop for Python Success: Top 10 US Python Development Providers
Your One-Stop Shop for Python Success: Top 10 US Python Development ProvidersYour One-Stop Shop for Python Success: Top 10 US Python Development Providers
Your One-Stop Shop for Python Success: Top 10 US Python Development Providers
 
Choosing The Best AWS Service For Your Website + API.pptx
Choosing The Best AWS Service For Your Website + API.pptxChoosing The Best AWS Service For Your Website + API.pptx
Choosing The Best AWS Service For Your Website + API.pptx
 
Infrastructure Challenges in Scaling RAG with Custom AI models
Infrastructure Challenges in Scaling RAG with Custom AI modelsInfrastructure Challenges in Scaling RAG with Custom AI models
Infrastructure Challenges in Scaling RAG with Custom AI models
 
AI-Powered Food Delivery Transforming App Development in Saudi Arabia.pdf
AI-Powered Food Delivery Transforming App Development in Saudi Arabia.pdfAI-Powered Food Delivery Transforming App Development in Saudi Arabia.pdf
AI-Powered Food Delivery Transforming App Development in Saudi Arabia.pdf
 
Microsoft - Power Platform_G.Aspiotis.pdf
Microsoft - Power Platform_G.Aspiotis.pdfMicrosoft - Power Platform_G.Aspiotis.pdf
Microsoft - Power Platform_G.Aspiotis.pdf
 
Ocean lotus Threat actors project by John Sitima 2024 (1).pptx
Ocean lotus Threat actors project by John Sitima 2024 (1).pptxOcean lotus Threat actors project by John Sitima 2024 (1).pptx
Ocean lotus Threat actors project by John Sitima 2024 (1).pptx
 
Monitoring and Managing Anomaly Detection on OpenShift.pdf
Monitoring and Managing Anomaly Detection on OpenShift.pdfMonitoring and Managing Anomaly Detection on OpenShift.pdf
Monitoring and Managing Anomaly Detection on OpenShift.pdf
 
“Building and Scaling AI Applications with the Nx AI Manager,” a Presentation...
“Building and Scaling AI Applications with the Nx AI Manager,” a Presentation...“Building and Scaling AI Applications with the Nx AI Manager,” a Presentation...
“Building and Scaling AI Applications with the Nx AI Manager,” a Presentation...
 
CAKE: Sharing Slices of Confidential Data on Blockchain
CAKE: Sharing Slices of Confidential Data on BlockchainCAKE: Sharing Slices of Confidential Data on Blockchain
CAKE: Sharing Slices of Confidential Data on Blockchain
 
GenAI Pilot Implementation in the organizations
GenAI Pilot Implementation in the organizationsGenAI Pilot Implementation in the organizations
GenAI Pilot Implementation in the organizations
 
How to use Firebase Data Connect For Flutter
How to use Firebase Data Connect For FlutterHow to use Firebase Data Connect For Flutter
How to use Firebase Data Connect For Flutter
 
Serial Arm Control in Real Time Presentation
Serial Arm Control in Real Time PresentationSerial Arm Control in Real Time Presentation
Serial Arm Control in Real Time Presentation
 
Uni Systems Copilot event_05062024_C.Vlachos.pdf
Uni Systems Copilot event_05062024_C.Vlachos.pdfUni Systems Copilot event_05062024_C.Vlachos.pdf
Uni Systems Copilot event_05062024_C.Vlachos.pdf
 

Planning Poker

  • 1. Dr. Mohammed Taha Abulhaija © mabulhaija © All Rights Reserved: The contents of all material available on these slides are copyrighted. You can always use this content or a part of it by referencing the author name.
  • 2. Sprint Planning Update Backlog Select & Refine User Stories Commit to Sprint Backlog 1 : 9 Duration Sprint Planning should not exceeds half a day of two-week sprint © mabulhaija
  • 3. Which Backlog User Story to include in the Sprint Backlog Story Story Story Sprint Planning © mabulhaija
  • 4. Great User Story Writing Planning Game: Poker The key to making a good Sprint Planning Work is two-fold Sprint Planning © mabulhaija
  • 5. Loudest voice in the room wins There's no timebox or time limit on discussions There's no way to tell when consensus is reached There's no way to improve without structure to improve upon Sprint Planning should not exceeds half a day of two-week sprint Sprint Planning © mabulhaija
  • 7. The Race – Knowing The Effort Target Training Efficiency Test KPI Result Why • 400m Race in 1:13 minute by maintaining a Sustainable Pace • Daily for one year • Every two weeks • Time the whole team needs to finish the 400m race • Efficiency Increased • Keep Training & Lessons Learning © mabulhaija
  • 8. NOW WE KNOW THAT Our Team can finish the race in 1 minute & 13 seconds 1 1.25 1.5 1.75 2 2.25 2.5 2.75 3 1 3 5 7 9 11 13 15 17 19 21 23 25 Time Each number represent two weeks Time to finish 400m race The Race By daily training: The team improve his time through the year from 3 to 1:13 minutes. ONE CONDITION applied – Sustainable Pace © mabulhaija
  • 9. The Race – Another Point View They have a FIXED PACE every day of training, How they improve their time? The team learns to:  Work Together  Trust & Support each other  Be Self Organized: remember no TRAINER  Review their Improvement every two weeks and CHANGED according to what they LEARN  Improved Frequently  Sustainable Improvement: after 6 months they reach SATISFACTION – an acceptable time  Always focus on their Target  They Enjoy their time and keep the training Simple NOW WE KNOW THAT The team now knows what EFFORT they should made to finish the 400m race in 1:13 minute, and HOW they can do that. © mabulhaija
  • 10. The Race – Another Point View  What The Team Do? They were being an AGILE Team. They Apply Agile 12 Principles to reach their target.  What Was The Team Learning Result? They knows what EFFORT they should made to reach their target. © mabulhaija The Team knows What they can do (Size of Work) in 01:13 minutes to PASS 400M Race
  • 11. The Race – Summary © mabulhaija Pass 400m RaceEffort Effort 01:13 Minute TheTeamTogether 01:13 Minute 01:13 Minute Pass 400m Race Pass 400m Race Effort ThisisWhattheteamneedstoknowto PLANtheirSPRINTBACKLOG
  • 12. The Race – Sustainable Pace What is the Sustainable Pace?  The Effort the team made Today Should not affect their performance Tomorrow.  The team should BALANCE their work, family and social life. i.e. is one at which the team can perform for very long periods of time – forever basically.  You can’t work all weekends in your life  You can’t have overtime forever  You can’t work tomorrow if you kill yourself today Sustainable Development Improvement Quality Delivery © Agile Gnostic © Bob Hartman © mabulhaija
  • 13. Planning Poker In Sprint Planning the team select stories from the Backlog to be in the Sprint Backlog, which means these Stories can be done (Implemented & Tested) within the Sprint Timebox, which mostly be Two Weeks and some times Four Weeks. © mabulhaija Sprint Backlog ?? ? ? How the team knows which Stories can be done within sprint
  • 14. Planning Poker Facts  The Team is an Agile Team – Applies Agile 12 Principles  The Team works on Sustainable Pace = No Overtime, No Weekends Work, . . .  Two-Week = 10 Working Days, and the working time is 8 Hours. i.e. Two- Week = 80 Working Hours. © mabulhaija  The team by Experience & Continuous Learning knows TOGETHER what Size of Work they can do in Two Weeks (80 Hours). f.g. let’s say 27 The Team Effort Value 27 Known by experience
  • 15. Planning Poker – Definition Planning Poker also called Scrum Poker, is a technique for ESTIMATING, mostly used to estimate Effort or Size of Work in Software Development. In Agile it used to estimate the Effort needed for each User Story in the Backlog. Note That: Stories in the Backlog are sorted by Product Owner based on its VALUE from the Customer point view. You don’t need to do estimation for all stories, just do it for stories you need in your Release. © mabulhaija
  • 16. Planning Poker – How In Planning Poker we use Fibonacci Cards It is a Playing Card of 6 sets, each set has its own color and each set (Color) has a cards with values following the Fibonacci Series (1, 2, 3, 5, 8, 13, 21) and the (?) card means I’m Not Sure, also a (Coffee Break) card for when it all gets a bit much! © mabulhaija
  • 17. Planning Poker – How Now the team use the cards to estimate the EFFORT (NOT Days or Hours) required by them as a Team Together, to implement Each Story, following this procedure: 1. Team BRIEFLY discuss a story 2. Everyone SILENTLY select a point card 3. Team reveals ALL selected cards in unison 4. If OUTLIERS exists, BRIEFLY discuss and Re-Vote © mabulhaija
  • 18. Planning Poker – How Now all User Stories has an Effort Value (Rate) and The Team knows what their Effort Value for Two-week Sprint. So, by basic Sum Calculations they choose which stories to be in the Sprint Backlog © mabulhaija © Jeff Dalton The Team Effort Value 27 Known by experience
  • 19. Planning Poker – Team Effort Team Effort enhanced by Experience, so its Effort Value also increased by Experience, until it reach its best. © mabulhaija 15 56 21 27 32 40 Enjoy The Estimation Game Good Luck!