Download - No estimates - a controversial way to improve estimation with results-handouts

#NoEstimates

Vasco Duarte@duarte_vasco

A way to improve estimates that gives you results!

Learn more about NoEstimates:

• How it can help you turn around a failing project

• How it can help you show what is possible and stick to that

• How it can help you find very early if you are late (and get your manager, or customer, to believe you)

• How to apply #NoEstimates without threatening anyone

Become a Beta Reader and get the book for free!

http://NoEstimatesBook.com



Vasco Duarte@duarte_vasco

http://bit.ly/vasco_blog

http://bit.ly/vasco_slideshare

[email protected]


#NoEstimates

pictoquotes

Kent Beck – Extreme Programming

Ken Schwaber - Scrum

Taiichi Ohno – Toyota Production System

Edwards W. Deming – Everything above...

“If I have seen further it is by standing on the shoulders of giants” - Isaac Newton

Just Google

it

Customer Collaboration over Contract NegotiationResponding to Change over Following a Plan

#NoEstimates is easy!

1.Select the most important piece of work you need to do

2.Break that work down into risk-neutral chunks of work

3.Develop each piece of work4.Iterate and refactor

#NoEstimates How-to

Is the system of development stable?

(ref: SPC)

I AM GOING TO GO AHEAD AND

ASK YOU TO DELIVER 10

STORIES NEXT SPRINT...

0

1

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3

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1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21

velocity

Average=

LCL

UCL

Target

Actual, measured throughput over 21 sprints

WTF!!!!!!#%&!

Can we use the data we observe to predict the system throughput and detect changes that affect system

stability?

1.Velocity outside limits 3 times in a row (“outside limits”)

2.There are 5 or more points in sequence (“run test”)

System stability rules

More in the 1-day #NoEstimates WorkshopInformation by email: [email protected]

0

2

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1 3 5 7 9 11 13 15 17 19 21

# of items/storiesdelivered

LCL

UCL

average

Team: AT

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1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16

LCL

UCL

average


Team: RF

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1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17

LCL

UCL

average


Team: RF2

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25

1 3 5 7 9 11 13 15 17 19 21


LCL

UCL

average

Team: SH

-1

1

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1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35

LCL

UCL

average


Team: K

0

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1 2 3 4 5 6 7 8


LCL

UCL

average

Team: MPC

0

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1 2 3 4 5 6 7 8 9 1011121314151617181920


LCL

UCL

average

Team: AS

0

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1 2 3 4 5 6 7 8 9 10


LCL

UCL

average

Team: FC

#NoEstimates delivers!

Counting Stories vs. Estimated Story Points

Q: Which ”metric” is more accurate when compared to

what actually happened in the project?

A long project

24Sprints

Which metric predicted most accurately the output of the

whole project?

a) After only the first 3 Sprints

b) After only the first 5 Sprints

Disclaimer...This is only one project!

Find 21 more at: http://bit.ly/NoEstimatesProjectsDB

After just 3 sprints

# of Stories predictive powerStory Points predictive power

The true output: 349,5 SPs

completed

The predictedoutput: 418 SPs

completed

+20%

The true output: 228 Stories

The predictedoutput: 220

Stories

-4%!

After just 5 sprints

# of Stories predictive powerStory Points predictive power

The true output: 349,5 SPs

completed

The predictedoutput: 396 SPs

completed

+13%

The true output: 228 Stories

The predictedoutput: 220

Stories

-4%!

Q: Which ”metric” is more accurate when compared to

what actually happened in the project?

But there is more...

#NoEstimates

RegularEstimates

“The chart is a snapshot of one team of 20+ teams over a 2 year period.” – Cory Foy

Which is more

predictable?

What difference does a Story Point make in a project that used both Story Points and

#NoEstimates?

Next you will see the forecasted release date when

using Story Points (values 1:3:5)

6871 71 71 71 71 71 72 72 72 73 73

0 3 7 7 9 11 12 13

20 20 22 23

0

10

20

30

40

50

60

70

80

90

100

1/3 1/17 1/31 2/14 2/28 3/14 3/28 4/11 4/25 5/9 5/23 6/6 6/20 7/4 7/18 8/1 8/15 8/29 9/12 9/26 10/10 10/24 11/7

Product Backlog Cumulative Flow Diagram

Remaining

Done

Linear (Remaining)

Linear (Done)

Release on 20th October

2014


using Story Points (values 1:2:3)

48

51 51 51 51 51 51 52 52 52 53 53

0 2 5 5 7 8 9 1015 15 17 18

0

10

20

30

40

50

60

70

80

1/3 1/17 1/31 2/14 2/28 3/14 3/28 4/11 4/25 5/9 5/23 6/6 6/20 7/4 7/18 8/1 8/15 8/29 9/12 9/26 10/10 10/24 11/7


Remaining

Done

Linear (Remaining)

Linear (Done)Release on

14th October 2014


#NoEstimates (or, all stories = 1 story point)

28

31 31 31 31 31 3132 32 32

33 33

0 1 3 35 5 6 7

10 1012 13

0

10

20

30

40

50

60

1/3 1/17 1/31 2/14 2/28 3/14 3/28 4/11 4/25 5/9 5/23 6/6 6/20 7/4 7/18 8/1 8/15 8/29 9/12 9/26 10/10 10/24 11/7


Remaining

Done

Linear (Remaining)

Linear (Done)

Release on 29thSeptember 2014

Conclusion...

All dates within 3 weeks of each other in a 38 to 42 week

project (a margin of ~8%)

Data used with permission from Bill Hanlon at Microsoft

”At that point, I stopped thinking that estimating

was important.”

Bill Hanlon: http://bit.ly/BHanlon

In 1986, Profs. S.D. Conte, H.E. Dunsmoir, andV.Y. Shen proposed that a good estimationapproach should provide estimates that arewithin 25% of the actual results 75% of the time

--Steve McConnel, Software Estimation: Demystifying the Black Art

In this presentation you have seen examples that far outperform what estimation specialists consider a ”good estimation”. In common they have one way to look at work: #NoEstimates

#NoEstimates testimonial

The deviation between estimated and actual velocity would have been approximately 15% lower if we would have used #NoEstimates.

We have analyzed data from 50 Sprints…

…at no time the story point based estimation was better than #NoEstimates.

One more thing...

80% Late or Failed

Source: Software Estimation by Steve McConnell

The larger the project, the bigger the problem


Comparison of 17 projects ending between 2001 and 2003. (Average: 62%)

Take #NoEstimates and experiment!

Learn, Be Agile!

Learn more about NoEstimates:

• How it can help you turn around a failing project

• How it can help you show what is possible and stick to that

• How it can help you find very early if you are late (and get your manager, or customer, to believe you)

• How to apply #NoEstimates without threatening anyone


Become a Beta Reader and get the book for free!

