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

http://NoEstimatesBook.com/



Vasco Duarte@duarte_vascohttp://bit.ly/vasco_bloghttp://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

Agility = Chaos!

Just Google

it

#NoEstimates

#Idealist#extremist#chaotic

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...

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 210

1

2

3

4

5

6

7

8

9

10

velocityAverage=LCLUCL

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]

1 3 5 7 9 11 13 15 17 19 210

2

4

6

8

10

12

# of items/stories deliveredLCLUCLaverage

Team: AT

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 160

2

4

6

8

10

12

LCLUCLaverage# of items/stories delivered

Team: RF

1 2 3 4 5 6 7 8 9 10111213141516170

2

4

6

8

10

12

14

16

18

20


Team: RF2

1 3 5 7 9 11 13 15 17 19 210

5

10

15

20

25


Team: SH

1 4 7 10 13 16 19 22 25 28 31 34-1

1

3

5

7

9

11

13

15

17


Team: K

1 2 3 4 5 6 7 80

2

4

6

8

10

12

14

16


Team: MPC

1 3 5 7 9 11 13 15 17 190

2

4

6

8

10

12

14

16


Team: AS

1 2 3 4 5 6 7 8 9 100

5

10

15

20

25

30


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? Real Data!

A long project

24Sprints

Which metric predicted most accurately the output of the

whole project?

a) After only the first 3 Sprintsb) 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 predicted output: 418 SPs

completed

+20%

The true output: 228 Stories

The predicted output: 220

Stories

-4%!

After just 5 sprints

# of Stories predictive powerStory Points predictive power

The true output: 349,5 SPs

completed

The predicted output: 396 SPs

completed

+13%

The true output: 228 Stories

The predicted output: 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)

1/3

1/10

1/17

1/24

1/31

2/7

2/14

2/21

2/28

3/7

3/14

3/21

3/28

4/4

4/11

4/18

4/25

5/2

5/9

5/16

5/23

5/30

6/6

6/13

6/20

6/27

7/4

7/11

7/18

7/25

8/1

8/8

8/15

8/22

8/29

9/5

9/12

9/19

9/26

10/3

10/10

10/17

10/24

10/31

11/7

0

10

20

30

40

50

60

70

80

6871 71 71 71 71 71 72 72 72 73 73

03

7 7 9 11 12 13

20 20 22 23

Product Backlog Cumulative Flow Diagram

RemainingLinear (Remaining)DoneLinear (Done)

Release on 20th October

2014


using Story Points (values 1:2:3)

1/3

1/10

1/17

1/24

1/31

2/7

2/14

2/21

2/28

3/7

3/14

3/21

3/28

4/4

4/11

4/18

4/25

5/2

5/9

5/16

5/23

5/30

6/6

6/13

6/20

6/27

7/4

7/11

7/18

7/25

8/1

8/8

8/15

8/22

8/29

9/5

9/12

9/19

9/26

10/3

10/10

10/17

10/24

10/31

11/7

0

10

20

30

40

50

60

48

51 51 51 51 51 5152 52 52

53 53

02

5 57 8 9 10

15 1517 18


RemainingLinear (Remaining)DoneLinear (Done)Release on

14th October 2014


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

1/3

1/10

1/17

1/24

1/31

2/7

2/14

2/21

2/28

3/7

3/14

3/21

3/28

4/4

4/11

4/18

4/25

5/2

5/9

5/16

5/23

5/30

6/6

6/13

6/20

6/27

7/4

7/11

7/18

7/25

8/1

8/8

8/15

8/22

8/29

9/5

9/12

9/19

9/26

10/3

10/10

10/17

10/24

10/31

11/7

0

5

10

15

20

25

30

35

28

31 31 31 31 31 3132 32 32

33 33

0 13 3

5 5 6 7

10 1012 13


RemainingLinear (Remaining)DoneLinear (Done)

Release on 29th September 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, and V.Y. Shen proposed that a good estimation approach should provide estimates that are within 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!




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

Internet

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