requirements engineering - werkcollege 2012: 05-estimating+planning

Leiden University. The university to discover.

Christoph Johann Stettina ([email protected])

Requirements Engineering Werkcollege Spring 2012 Session 5: EsBmaBng & Planning


Session 5: EsBmaBng & Planning

Today:

•  Es$ma$ng: Planning Poker •  Planning: Backlogs

Why is it important? •  Projects o5en overrun cost es$mates •  Effort es$ma$on difficult in prac$ce due to

complexity of tasks and differences in implementa$on speed of teams


Part 1 – EsBmaBon Planning Poker


Session 5: EsBmaBon & Planning

Interface between crea$ng requirements and laying out the so5ware development process

EsBmaBng – Es$ma$ng the [resources, $me, size] required to develop a [user story, feature, or requirement]

Planning – PuIng the es$mates together to formulate a project plan and schedule -‐-‐ Cohn (2005)


Concepts of EsBmaBng Size (Cohn, 2005) Story Points •  Unit of measure for the overall size of a user story •  Not related to the amount of $me or people Ideal Bme •  The amount of $me excluding peripheral ac$vi$es •  Example: American football game = 60 minutes Elapsed Bme •  Time that passes on the clock to complete it •  Example: American football game = 3 hours Velocity •  Measure of a team’s rate of progress


Deriving a plan (Cohn, 2005)

Story Story

Story

Story Story

Story

Desired Features

Estimate Size

Divide by Velocity

Derive Duration

Prioritize

Create Iteration

Plan


Deriving a plan (Cohn, 2005)

Story Story

Story

Story Story

Story

Desired Features

Estimate Size

Divide by Velocity

Derive Duration

Prioritize

Create Iteration

Plan

5 Story Points /Iteration

30 Story Points ≈ 6 Iterations

≈ May 10


How to esBmate?

Pragma$c view:

Beyond a certain point addi$onal effort spent on es$ma$on yields liZle addi$onal value -‐-‐ Cohn (2005)

Accuracy

Effort


EsBmaBng Story Points (Cohn, 2005) Take a medium-‐size story and assign it a “5” EsBmate the other stories accordingly •  Twice as big •  Half as big •  Almost but not quite as big •  A liZle bit bigger

Use the following sizes for the stories:

•  0, 1, 2, 3, 5, 8, 13, 20, 40, 100

Close to one iteration

“story” Few iterations large

“epic”


Planning Poker (Fægri, 2010)

Planning Poker Cards

•  0, 1, 2, 3, 5, 8, 13, 20, 40, 100 •  Fibonacci sequence: Reflect on uncertainty in

es$ma$ng larger items

Special Cards

•  Zero: “Just a few minutes of work” •  Ques$on mark: “No Idea at all” •  Coffee: “I’m too 7red to think. Let’s take a break.”


•  Each task to be es$mated is presented by the Product Owner.

•  The group members ask for clarifica$ons in order to es$mate the task effort. (Timeboxed discussion)

•  Upon no$fica$on from the Scrum Master each member presents the card showing the es$mate believed to best approximate the task effort.

•  If the es$mates are roughly similar the Scrum Master uses the most frequent es$mate as the task es$mate.

•  If there is wide disagreement in the es$mates the task is discussed again and the group members present updated es$mates. (Fægri, 2010)

Planning Poker: Rules


EsBmaBon: In-‐class assignment

Planning Poker Exercise

•  Create groups of 4-‐9 •  Discuss the received case study •  Es$mate the size of user stories •  Goal: Create a list according to size


Planning Poker

Advantages

•  Mul$ple expert opinions •  Discussion leads to more accurate es$ma$ons •  More fun!

Disadvantages

•  Mee$ngs with en$re team more expensive •  Moderator needs to $mebox discussions •  Dominant personali$es and poli$cs can

interfere


Part 2 – Planning Backlogs


Planning: Agile Development Cycle

!"#$%&'$()*+',,$-$!"#".$/+)01$234$5167)7+28$()*+',,$9*3:)*8

!"#"$%&'()*%+,-%.*/0(0'+1%2(3'455%63,7(31

/+)01$&2,$373'$6)',+)7;'4$)*8',$234$6)2+:7+',$6)*<7473=$:&'$;2,7+$,':06"$%&*,'$2)'$:&'$4'>73'4$)*8',$

>*)$:&'$%'21?$:&'$/+)01@2,:')$234$()*40+:$AB3')?$:&'$:&)''$+'3:)28$1'':73=$:C6',$2,$:&'$/6)73:$

6823373=$1'':73=?$:&'$D278C$/+)01$234$:&'$/6)73:$)'<7'B?$2,$B'88$2,$:&'$)'E07)'4$;2,7+$2):7>2+:,$

321'8C$:&'$()*40+:$F2+G8*=?$:&'$/6)73:$F2+G8*=$234$:&'$F0)34*B3$+&2):$HI37;')=?$!JJKL"

!""#$%&'%()*+,-+./0+12&#3+4&)20$$

!"#$%&'#(

M,$6*73:'4$:*$B7:&73$:&'$73:)*40+:7*3$:*$:&7,$:&',7,$2:$7:,$>7),:$62='?$/+)01$4'>73',$+'):273$)*8',$

682C73=$62):$B7:&73$:&'$6)*+',,"$D7,:73=07,&73=$;':B''3$67=,$234$+&7+G'3$7:$4'>73',$:&'$7347<74028,$

;'73=$N+*117::'4N$234$:&*,'$*38C$N73<*8<'4N"$%&7,$47<7,7*3$73:*$:B*$62):,$12)G,$:&'$7347<74028,$

47)'+:8C$62):7+762:73=$73$:&'$4'<'8*61'3:$*>$:&'$6)*40+:$234$:&'$6)*O'+:P,$,0++',,?$234$:&*,'$;'73=$

73:')',:'4$234$73<*8<'4$;0:$*38C$7347)'+:8C$2>>'+:'4$;C$:&'$6)*O'+:P,$,0++',,"

/:2))73=$:&'$:&'$:'21?$:&'$/+)01@2,:')$234$:&'$()*40+:$AB3')?$:&'$N67=,N$2)'$)',6*3,7;8'$*>$:&'$

6)*40+:$+)'2:7*3"$%&'$:'21$:C67+288C$+*3,7,:,$*>$.-K$6'*68'$B7:&$+)*,,->03+:7*328$,G788,$234$7,$73$

+&2)='$*>$4',7=3? $4'<'8*61'3:? $:',:73=$234$4'87<')C" $%&'$/+)01@2,:')$ 7, $,*1'B&2:$2$3'B$)*8'$

4'<'8*6'4"$Q7:&$2$:')1$4'87;')2:'8C$+&*,'3$:*$47,:73=07,&$&7,$)*8'$>)*1$:&2:$*>$2$3*)128$6)*O'+:$

1232=') $ :&' $/+)01@2,:') $ 7, $1*)' $ *> $ 2 $ >2+787:2:*) $ *) $ +*2+& $ :* $ :&' $ :'21 $2, $ :&' $ :'21$ 7, $ ,'8>-

*)=237R73="$S'$7,$'3,0)73=$:&2:$:&'$)08',$*>$/+)01$2)'$>*88*B'4?$)'1*<',$716'471'3:,$>)*1$:&'$

:'21$234$7,$73$+&2)='$*>$7168'1'3:73=$/+)01$B7:&73$:&'$*)=237R2:7*3P,$+08:0)'$H/+&B2;')?$!JJTL"$

%&'$()*40+:$AB3')$7,$:&'$<*7+'$*>$:&'$+0,:*1')$*3$,7:'?$,71782)8C$:*$U($H+*162)'$:*$82,:$,'+:7*3L"$

S'$B)7:',$:&'$V,')$/:*)7',$234$6)7*)7:7R',$:&'1$B7:&73$:&'$()*40+:$F2+G8*="

%&'$N+&7+G'3N $2)' $2+:73= $ 7347)'+:8C $40)73= $ :&' $682C? $ :&'C $2)'$+*3,7,:73= $*> $288 $ :&'$,:2G'&*84'),$

#.

!"#$%&'(&)*+,-#.(,/&0'(1

,#(,2*3#4(&56&7+8#9'#)%,)#,7,-89*9:#3&9*;7:#+'3*7;#,73#)&9)*7;#%,22&79#,-/'9)#9*/6-),7&'69-8"#<7#=>:#

)%*9#*9#*/2-&/&7)&3#)%('6;%#9/,--#*)&(,)*'7#-&7;)%9#'4#/,?*/,-#'7&#/'7)%#0%*-&#3'*7;#,#@*) #'4#

&A&(8)%*7;#0*)%*7#&,+%#*)&(,)*'7"#=>#3&9*;7&3#4'(#)&,/9#4('/#!#)'#BC#/&/@&(9#3&4*7&9#3*9)*7+)*A&#

2%,9&9#'4#,#2('D&+):#('-&9#,73#(&92'79*@*-*)*&9#,++'/2,7*&3#@8#2(,+)*+&9#96+%#,9#2,*(#2(';(,//*7;#

E)0'#2&'2-&#0(*)*7;#+'3&#'7#)%&#9,/&#+'/26)&(#(&A*&0*7;#&,+%#')%&(F:#+'--&+)*A&#+'3&#'07&(9%*2#

E&A&(8'7&#*9#(&92'79*@-&#4'(#,--#)%&#+'3&:#&A&(8'7&#*9#,--'0&3#)'#+%,7;&#*)F:#)&9)G3(*A&7#3&A&-'2/&7)#

E9%'()#*)&(,)*'79#,73#2(&G0(*))&7#)&9)#+,9&9F:#'7G9*)&#+69)'/&(:#+'7)*76'69#*7)&;(,)*'7:#(&4,+)'(*7;#,73#

9*/2-&#3&9*;7#EH&+1:#!CCIF"

!""#$%&'%()*+,-+./+"(01232"1+456)&1+'*7+8'&71*9+:;;<=

J?)(&/&#2(';(,//*7;#*9#EK8@L#,73#K*7;9M8(:#!CCNF#)%&#/'9)#0*3&-8#9)63*&3#,;*-&#/&)%'3'-';8#*7#

(&9&,(+%"#O7&#'4#=>#2(&/*9&9#*9#)%&#&-*/*7,)*'7#'4#(&56*(&/&7)9:#%&,A80&*;%)#3&9*;7#,73#)&9)*7;#

2%,9&9 # ,73 # 4'(/,- # 3'+6/&7),)*'7 # )%('6;% # 9&,/-&99 # *7)&;(,)*'7 #0*)% # *74'(/,- # ,73 # +'79),7)-8#

,3'2)&3#(&56*(&/&7)9"#P(*)*+9#%'0&A&(:#,(;6&#)%,)#@8#%,A*7;#)%&#'7G9*)&#+69)'/&(#+,7#-&,3#)'#+'9)-8#

(&0'(1#,73#2('D&+)#9+'2&#+(&&2#@&8'73#,--#,;(&&3#/&,79"

!"#$%&'(!')&"

P(89),- #P-&,( # *9 #'7& #/&/@&(#'4 #, # (,7;&#'4 #/&)%'3'-';*&9 #+'79*9)*7; #'4 #P(89),- #P-&,(: #P(89),-#

Q&--'0:#P(89),-#O(,7;&#,73#P(89),-#R&3"#S&-&+)&3#,++'(3*7;#)'#)%&#9*T&#,73#+(*)*+,-*)8#'4#)%&#2('D&+):#

)%&#P(89),- #4,/*-8#(,7;&9#4('/#P(89),- #P-&,(#,9#/'9) #96*),@-&# 4'(#9/,-- #,73#9*/2-&#2('D&+)9 # )'#

P(89),- #R&3 # ),(;&)*7; # @*; # ,73 # +(*)*+,- # 2('D&+)9" #O7-8 #P(89),- #P-&,( # ,73 #P(89),- #O(,7;& #0&(&#

92&+*4*&3#*7#3&),*-#EP'+1@6(7:#!CCBF"

P(89),-#P-&,(#%,9#@&&7#3&A&-'2&3#4'(#9/,--#2('D&+)9#'4#62#)'#9*?#3&A&-'2&(9#,73#3&9+(*@&9#('-&9:#

0'(1 # 2('36+)9 # ,73 # 2'-*+*&9: # @6) # ,A'*39 # 2('+&99&9 # '( # ,()*4,+)9" #$%& #/&)%'3'-';8 # (&56*(&9 # )%&#

4'--'0*7;#2('2&()*&9U #4(&56&7) #3&-*A&(8#'4#69,@-&#+'3&#)'#69&(9#0*)%# *7+(&/&7)9#@&)0&&7#V#)'#B#

/'7)%9#EI#/,?*/6/F:#(&4-&+)*A&#*/2('A&/&7)#E2(&G#,73#2'9)G*7+(&/&7)#0'(19%'29F#,73#&/@(,+*7;#

'9/')*+#+'//67*+,)*'7#0*)%#+'-'+,)*'7#2(&4&((&3"#P(89),- #P-&,(: #%'0&A&(: #3'&9#7')#(&56*(&#,78#

3'+6/&7),)*'7#)'#@&#7&+&99,(*-8#+(&,)&3"

*+),(-,./.)0(1"23)$$(4*+),-15

O2&7W>#4'(/&(-8#0,9#,-9'#(&4&((&3#)'#,9#O2&7W>XH,9*+#4'(#@&*7;#)%&#/'9)#,;*-&#,73#-*;%)0&*;%)#

4'(/#'4#)%&#'2&7#9'6(+&#9'4)0,(&#3&A&-'2/&7)#2('+&99#3&A&-'2&3#,9#2,()#'4#)%&#J+-*29&#>('+&99#

.(,/&0'(1"#<)9&-4#@&*7;#@,9&3#'7#)%&#H,9*+#W7*4*&3#>('+&99#EHW>F:#O2&7W>#-&,A&9#'6)#)%&#/'9)#

V!


Planning: Backlogs

Product Backlog

•  All func$onality required in the product •  User stories and requirements created by team •  Priori$zed according to Return on Investment Sprint / IteraBon Backlog

•  Contents of a Product Backlog selected for a “poten$ally shippable product increment”


Product Backlog (Schwaber, 2004)

•  Repriori$zed every itera$on •  Evolves with the product •  Owned by the sponsor, Product Owner

Story Name

Init Est.

Adj. Factor

Adj. Est.

1

2

3

4

5

Story 1 3 0.2 3.6 3.6 0 0 0 0

Story 2 2 0.2 2.4 2.4 0 0 0 0

Story 3 3 0.2 3.6 3.6 0 0 0 0

Story 4 3 0.2 3.4 3.4 0 0 0 0

-‐-‐ SPRINT 1 11 0.2 13 13 0 0 0 0

Story 5 3 0.2 3.6 3.6 3.6 0 0 0

Story 6 2 0.2 2.4 2.4 2.4 0 0 0


Story Name

Init Est.

Adj. Factor

Adj. Est.

1

2

3

4

5

Story 1 3 0.2 3.6 3.6 0 0 0 0

Story 2 2 0.2 2.4 2.4 0 0 0 0

Story 3 3 0.2 3.6 3.6 0 0 0 0

Story 4 3 0.2 3.4 3.4 0 0 0 0

-‐-‐ SPRINT 1 11 0.2 13 13 0 0 0 0

Story 5 3 0.2 3.6 3.6 3.6 0 0 0

Story 6 2 0.2 2.4 2.4 2.4 0 0 0

Product Backlog Adjusted Estimate

Initial Estimate

Complexity factor

Sprints


IteraBon Backlog

•  Each itera$on the team picks the top priority items from the Product Backlog

•  Contents of a Sprint Backlog should be a part presentable at the end of a sprint

Task Name

Originator Responsible Status Mo

Tu

We

Th

Fr

Task 1 Danielle Danielle Completed 20 0 0 0 0

Task 2 Jim Allen NotStarted 8 8 8 8 8

Task 3 Tom Completed 12 0 0 0 0

Task 4 George In Progress 24 24 24 24 12

Task 5 Tim Completed 12 12 12 12 12

Task 6 Josh In Progress 12 10 10

Task 7 Danielle In Progress 24 24 24 24 12


IteraBon Backlog (Schwaber, 2004)

Task Name

Originator Responsible Status Mo

Tu

We

Th

Fr

Task 1 Danielle Danielle Completed 20 0 0 0 0

Task 2 Jim Allen NotStarted 8 8 8 8 8

Task 3 Tom Completed 12 0 0 0 0

Task 4 George In Progress 24 24 24 24 12

Task 5 Tim Completed 12 12 12 12 12

Task 6 Josh In Progress 12 10 10

Task 7 Danielle In Progress 24 24 24 24 12

Days


Planning: In-‐class assignment

Product and IteraBon Backlogs

1.  Priori$ze stories according to business needs 2.  Create a Product Backlog

3.  Select top priority Product Backlog items 4.  Think of actual tasks evolving from the stories 5.  Create a Itera$on Backlog


Bibliography •  Cohn, M. (2005). Agile Es$ma$ng and Planning. Pren$ce Hall PTR.

•  Fægri, T. E. (2010) “Adop$on of team es$ma$on in a specialist organiza$onal environment,” in In proceedings of XP2010, 11th Interna$onal Conference, Trondheim, Norway, ser. LNBIP, vol. 48

•  Fowler, M. (2004), UML dis$lled: a brief guide to the standard object modeling language (3 ed.), Addison-‐Wesley, p. 131, ISBN 9780321193681

•  Hammer, M. & Champy, J. (1994). Reengineering the corpora$on: A manifesto for business revolu$on. New York: Harper.

•  IEEE, So5ware Engineering CommiZee (1998) IEEE Recommended Prac$ce for So5ware Design Descrip$ons. ANSI/IEEE Std 830, October 1998

•  Keil, M., Carmel, E. Customer-‐Developer Links in So5ware Development. Communica$ons of the ACM, 38 (5): 33–44, 1995.

•  van Lamsweerde, A. (2009) Requirements Engineering: From System Goals to UML Models to So5ware Specifica$ons. Wiley, March 2009.

•  Schwaber, K., Agile Project Management with Scrum, Microso5 Press, 2004

requirements engineering - werkcollege 2012: 05-estimating+planning

Education