Software Project Estimation - Euro Project Office

Software Project EstimationSoftware Project Estimation

Statistical Methods for Software Measurements MetriKon 2009, Kaiserslautern

Statistical Methods for Software Measurements MetriKon 2009, Kaiserslautern

www.e-p-o.com

November 19, 2009Software Project Estimations– Slide 2 © Copyright 2009: Euro Project Office AG

Dr. Thomas FehlmannDr. Thomas Fehlmann 1981: Dr. Math. ETHZ

Mathematical Logic, Combinatory Logic 1982-89: Manager Software–Development Datacolor AG

Color Quality Management 1990-95: Senior Consultant – Project Office DEC CH

Six Sigma Black Belt for Systems Integration 1996-99: Sales Support Manager – Proposal Center

Unisys Schweiz and Italy 1999ff: Euro Project Office AG, Zürich

Project Management, Coaching & Support SwiSMA: Software – Metrics, Function Points, COSMIC FFP Effort & Defect Prediction for SW Project Akao–Price 2001 for original contributions to QFD Member of the Board of QFD Institute Germany – QFD Architect Six Sigma Black Belt for GMC Software AG Master Black Belt for Siemens Building Technologies

1981: Dr. Math. ETHZ Mathematical Logic, Combinatory Logic

1982-89: Manager Software–Development Datacolor AG Color Quality Management

1990-95: Senior Consultant – Project Office DEC CH Six Sigma Black Belt for Systems Integration

1996-99: Sales Support Manager – Proposal Center Unisys Schweiz and Italy

1999ff: Euro Project Office AG, Zürich Project Management, Coaching & Support SwiSMA: Software – Metrics, Function Points, COSMIC FFP Effort & Defect Prediction for SW Project Akao–Price 2001 for original contributions to QFD Member of the Board of QFD Institute Germany – QFD Architect Six Sigma Black Belt for GMC Software AG Master Black Belt for Siemens Building Technologies


AgendaAgenda

What is Six Sigma? Measuring Software – Sizing Requirements Measuring Software – Cost Drivers Software Projects Estimations Conclusions



AgendaAgenda




What is Six Sigma?What is Six Sigma?

1. Focus on Customer‘s Needs2. Make Processes defect–free3. Manage quantitatively

1. Focus on Customer‘s Needs2. Make Processes defect–free3. Manage quantitatively

σSixSigma


LSLLSL USLUSL

Statistical Process ControlStatistical Process Control

OutputOutputOutputOutputOutputOutputOutputOutputOutputOutputOutputOutputInputInput OutputOutput

SpecificationsChecklistsTemplates


MethodsTools

ICT–Support

MethodsTools

ICT–Support

ProcessProcess

Resou rcesResou rcesResou rcesResou rces

OutputOutput

6σ 6σ Defects outside tolerance!Defects outside tolerance!

= 1/6 = 1/6 −−

66 σσ== LSLLSLUSLUSLCpCp


LSLLSL USLUSL

Statistical Process ControlStatistical Process Control

OutputOutputOutputOutputInputInput



MethodsTools

ICT–Support

MethodsTools

ICT–Support

ProcessProcess

Resou rcesResou rcesResou rcesResou rces

−−66 σσ

== LSLLSLUSLUSLCpCp

6σ 6σ Defects within tolerance!Defects within tolerance!

> 1> 1−−66 σσ

== LSLLSLUSLUSLCpCp


Transfer FunctionsTransfer Functions

y = M(x): The Solution x yields the y = M(x) as a response to Business Needs M-1 is inverse transfer function M-1 predicts the solution x that

yields y = M(x), given Business Needs y: x = M-1(y) For a matrix representation of M, MT is the transposed matrix

MT approximates M-1 if x is an Eigenvector for MT°M

y = M(x): The Solution x yields the y = M(x) as a response to Business Needs M-1 is inverse transfer function M-1 predicts the solution x that

yields y = M(x), given Business Needs y: x = M-1(y) For a matrix representation of M, MT is the transposed matrix

MT approximates M-1 if x is an Eigenvector for MT°M

Technical Solution (x ) → Business Needs (y)Technical Solution (x ) → Business Needs (y)

xx

y =

f(x)

y =

f(x)


Use of Statistical Methods - Use of Statistical Methods - The Google MatrixThe Google Matrix

Search Measured Frequency ProfileOccurrences

URL

5

3

9 3

9

9

3

URL

1

URL

2

URL

3

URL

4

9

9

1 1

91

Q1Q1

Q2Q2

Q3Q3

Q5Q5

Q4Q4

Popularityof URL Content Index


Cost Drivers for SW ProjectsCost Drivers for SW ProjectsCause/Effect matrixCause/Effect matrix

Phases Measured Effort Profile

RDRD

TSTS

QAQA

PMPM

“a”-Parameters

? = unknown relation= unknown relation

Measured Cost Driver Profile

Team

Func

. Size

Req.

Vol

.

Tech

.

Com

m.

PIPI

? ?

??

? ? ?

????

? ?

???

?

? ? ?

?? ???


Elimination of VarianceElimination of VarianceCause/Effect matrixCause/Effect matrix

Phases Measured Effort Profile“a”-Parameters

Tech

.

Com

m.

Team

Best approximation bySix Sigma Eigenvector

= strong relation= strong relation= medium relation= medium relation= weak relation= weak relation

3

9 3

9

9

3

9

13

Func

. Size

Req.

Vol

.

Tech

.

Com

m.

Req.

Vol

.

Tech

.

Com

m.

Func

. Size

Req.

Vol

.

Tech

.

Com

m.

9

9

1 1

91

RDRD

TSTS

QAQA

PMPM

PIPI


Critical Parameters forCritical Parameters forTransfer FunctionsTransfer Functions

Select Critical Parameters Representative profiles for Business Needs and

Technical Solution requirements Given technical requirements profile x = <ξ1,

…,ξn>, response profile to business needs is y = M(x) = <ϕ1(x), …, ϕm(x)>

You can always limit the number of Critical Parameters to

7 ± 2

Select Critical Parameters Representative profiles for Business Needs and

Technical Solution requirements Given technical requirements profile x = <ξ1,

…,ξn>, response profile to business needs is y = M(x) = <ϕ1(x), …, ϕm(x)>

You can always limit the number of Critical Parameters to

7 ± 2


Prof. Daniel Kressner, ETHZProf. Daniel Kressner, ETHZ Seminar für Angewandte Mathematik (SAM)

HG G 58.1, Rämistrasse 101 8092 Zürich, Switzerland

Email: [email protected] Phone: +41 44 632 8710 News Web site of the Numerical Linear Algebra Group

available: http://www.sam.math.ethz.ch/NLAgroup/ Book: Numerical Methods for General and Structured

Eigenvalue Problems

Seminar für Angewandte Mathematik (SAM)HG G 58.1, Rämistrasse 101 8092 Zürich, Switzerland

Email: [email protected] Phone: +41 44 632 8710 News Web site of the Numerical Linear Algebra Group

available: http://www.sam.math.ethz.ch/NLAgroup/ Book: Numerical Methods for General and Structured

Eigenvalue Problems

http://www.amazon.com/exec/obidos/tg/detail/-/3540245464/qid=1127725187/sr=8-2/ref=sr_8_xs_ap_i2_xgl14/104-8802870-9139901?v=glance&s=books&n=507846


An Application of the Google Matrix An Application of the Google Matrix (Eigenvectors)(Eigenvectors)

GMC Software Technology A Swiss company, leading in Customer

Communication Software TransPromo – joining promotional and

transactional messages GMC Analytics is using the Google Matrix for

matching promotional with transactional messages

GMC Software Technology A Swiss company, leading in Customer

Communication Software TransPromo – joining promotional and

transactional messages GMC Analytics is using the Google Matrix for

matching promotional with transactional messages


PersonalizationPersonalization

Message Message EffectivenessEffectiveness

Individual MarketingIndividual Marketing

Anonymous Shooting in the Dark Spray and Pray Unmotivated contact

Interpreted Data Mining Predictive Analytics

Known Preferences Feedback Drivers & Motives Loyalty & Commitment Engagement

Monologue

Conversational


The Solution – GMC’s ValueThe Solution – GMC’s Value


In SummaryIn Summary GMC Analytics gives within weeks actionable and highly

effective data to marketing executives A unique competitive advantage - GMC Analytics enables

marketers to recognize the drivers and motives of the customer base

An easy-to-work-with instrument to analyze customer preferences in a large customer portfolio

GMC Analytics enables a conversation with the customer A reliable mechanism to measure and monitor the

campaign performance

GMC Analytics gives within weeks actionable and highly effective data to marketing executives

A unique competitive advantage - GMC Analytics enables marketers to recognize the drivers and motives of the customer base

An easy-to-work-with instrument to analyze customer preferences in a large customer portfolio

GMC Analytics enables a conversation with the customer A reliable mechanism to measure and monitor the

campaign performance

Measure – Don’t AssumeEliminate Variations – Search Eigenvectors

Measure – Don’t AssumeEliminate Variations – Search Eigenvectors


AgendaAgenda




Functional Size ofFunctional Size ofBusiness RequirementsBusiness Requirements

IFPUG Functional Size (ISO/IEC 20926:2003) is easily derived from Use Case Analysis, or from the User Manual Good choice for

understanding and sizing user requirements Business Requirements

Sizing Unit (UFP = Unadjusted FP)

IFPUG Functional Size (ISO/IEC 20926:2003) is easily derived from Use Case Analysis, or from the User Manual Good choice for

understanding and sizing user requirements Business Requirements

Sizing Unit (UFP = Unadjusted FP)


Functional Size ofFunctional Size ofTechnical Requirements Technical Requirements

The COSMIC Full Function Points metrics (ISO/IEC 19761:2003) size requirements from different viewpoints in COSMIC functional size units (Cfsu) No. of Entry/Exits to/from Functional Processes No. of Read and Writes to/from Data Groups (Storage)

The COSMIC Full Function Points metrics (ISO/IEC 19761:2003) size requirements from different viewpoints in COSMIC functional size units (Cfsu) No. of Entry/Exits to/from Functional Processes No. of Read and Writes to/from Data Groups (Storage)

FunctionalFunctionalProcessProcess

Data

Grou

pDa

ta Gr

oup

Data

Grou

pDa

ta Gr

oup

User

Dev

ice, o

rUs

er D

evice

, or

Engin

eere

d Dev

iceEn

ginee

red D

evice

User

Dev

ice, o

rUs

er D

evice

, or

Engin

eere

d Dev

iceEn

ginee

red D

evice

Exit (X)Exit (X)Exit (X)Exit (X)Entry (E)Entry (E)Entry (E)Entry (E)

Exit (X)Exit (X)Exit (X)Exit (X)Entry (E)Entry (E)Entry (E)Entry (E)

Read (R)Read (R)Read (R)Read (R)Write (W)Write (W)Write (W)Write (W)


Technical ComplexityTechnical Complexity

There is no suitable sizing method IFPUG: System environment defines Complexity “System Characteristics” UPF FP

ISBSG: Industry defines Complexity Software Benchmarking by Industry PDR = Product Delivery Rate h/F

There is no suitable sizing method IFPUG: System environment defines Complexity “System Characteristics” UPF FP

ISBSG: Industry defines Complexity Software Benchmarking by Industry PDR = Product Delivery Rate h/F


AgendaAgenda




Use of Statistical MethodsUse of Statistical Methods

The ISBSG Repository Current version is R11 and contains over 5’000

projects This is sufficient amount of data to do statistics! ISBSG compares only apples with apples Establishing categories of industry and application area

Technical Complexity and other Cost Drivers cause the PDR differences among industries!

The ISBSG Repository Current version is R11 and contains over 5’000

projects This is sufficient amount of data to do statistics! ISBSG compares only apples with apples Establishing categories of industry and application area

Technical Complexity and other Cost Drivers cause the PDR differences among industries!


Cost Drivers – MeasurableCost Drivers – Measurable Functional Functional Size; for all modules involved Appropriate viewpoints for Functional User Requirements (FURs)

Non-Functional Requirements Volatility – by number of change requests Technical Complexity – by impact on other modules Communication Needs – by number of stakeholders Team Size Time Constraints – usually the only metrics known Availability Requirements – by mean time between failure Reliability Requirements – by test coverage Documentation Requirements – Low/Medium/High

Functional Functional Size; for all modules involved Appropriate viewpoints for Functional User Requirements (FURs)

Non-Functional Requirements Volatility – by number of change requests Technical Complexity – by impact on other modules Communication Needs – by number of stakeholders Team Size Time Constraints – usually the only metrics known Availability Requirements – by mean time between failure Reliability Requirements – by test coverage Documentation Requirements – Low/Medium/High

Low Medium High

0.5 1.0 1.5

Size Drivers Size Drivers

Cost Drivers Cost Drivers


Cost Drivers have different impact Cost Drivers have different impact depending on Project Phasedepending on Project Phase

RD – Requirements Elicitation TS – Design, Development & Unit Tests QA – VERification and VALidation PI – Integration and Documentation PM – Project Management (CM, ReqM, PPQM, …)

RD – Requirements Elicitation TS – Design, Development & Unit Tests QA – VERification and VALidation PI – Integration and Documentation PM – Project Management (CM, ReqM, PPQM, …)


How it worksHow it works

Depending upon industry or application area: Scope Manager measures your Cost Drivers

High – Medium – Low profile, or values in between Project Office measures effort per phase (RD,TS,QA,PI,PM)

You can use Expert Estimations instead Or consult the ISBSG Repository R11

Do this for a number of Estimation Items Then calculate the Six Sigma Eigenvector

Multiple regression analysis Similar to the Google Matrix PageRank algorithm

Elimination of Variance yields Strength of dependency between Cost Driver and phase effort –

called “a”-Parameters Characteristically for industry or application area

Actual effort values replace prediction when available Self-Calibration

Depending upon industry or application area: Scope Manager measures your Cost Drivers

High – Medium – Low profile, or values in between Project Office measures effort per phase (RD,TS,QA,PI,PM)

You can use Expert Estimations instead Or consult the ISBSG Repository R11

Do this for a number of Estimation Items Then calculate the Six Sigma Eigenvector

Multiple regression analysis Similar to the Google Matrix PageRank algorithm

Elimination of Variance yields Strength of dependency between Cost Driver and phase effort –

called “a”-Parameters Characteristically for industry or application area

Actual effort values replace prediction when available Self-Calibration

Cost Driver Cost Driver

Phas

e 2 =

TSPh

ase 2

= TS

Phas

e 1 =R

D

Phas

e 1 =R

D

LowLow MediumMedium

Cost DriverCost Driver

Perso

n Day

sPe

rson D

ays

HighHigh

“a”–Parametersdescribe slope

“a”–Parametersdescribe slope


Estimation FormulaEstimation Formula

(#Module1*eSizeDrive1 + #Module2*SizeDriver2 + …)*

#Measurement3*eCostDriver3

*#Measurement4*eCostDriver4

*…*

#Measurementn*eCostDrivern

(#Module1*eSizeDrive1 + #Module2*SizeDriver2 + …)*

#Measurement3*eCostDriver3

*#Measurement4*eCostDriver4

*…*

#Measurementn*eCostDrivern


Example: Six Sigma for Example: Six Sigma for Effort EstimationsEffort Estimations

Collect all estimations within an application and calibrate them against actuals

Use cost driver profiles to identify common estimation patterns within the applications

Compare estimations based on cost drivers

Collect all estimations within an application and calibrate them against actuals

Use cost driver profiles to identify common estimation patterns within the applications

Compare estimations based on cost driversApplication: Estimation ID Estimation Item Description: Release: Based on: Estimation Point:CmmiEstimation-MIS

Expert Estimator: Reviewer: Approver:Initial

Mid

Final

Cost Driver Impact Profile Cost Driver ID: Cost Driver (unit): Low Medium High1.5 M01 Functional Size (uFP): 1208 0.5 1.0 1.5

1.0 M02 Requirements Volatility (#CR): 3 0.5 1.0 1.5

1.5 M03 Technical Complexity (L-M-H): 1.5 0.5 1.0 1.5

1.0 M04 Communication Needs (#Sh): 1 0.5 1.0 1.5

1.0 M05 Team Size (#Tm): 3.5 0.5 1.0 1.5

1.5 M06 Time Constraints (#day): 400 0.5 1.0 1.5

1.0 M07 Availability Requirements (#h): 1 0.5 1.0 1.5

1.0 M08 Reliability Requirements (%): 1 0.5 1.0 1.5

1.0 M09 Documentation Requirements (L-M-H): 3 0.5 1.0 1.5

RD – Requirements Elicitation:TS – Design, Development & Unit Tests:

QA – VERification and VALidation:401 Days132 Days

Final

Estimation Date:Status:

R10ISBSG-26661 Case Management

Approved 11-Nov-2001

231 Days

M01 M02 M03 M04 M05 M06 M07 M08 M09

High

M edium

Low

Application: Estim ation ID Estim ation I tem Description: Release: Based on: E stim ation Point:CmmiEstimation-MIS

E xpert E stim ator: Reviewer: Approver:Initial

Mid

Final

Cost Driver Im pact P rofile Cost Driver ID: Cost Driver (unit): Low Medium High0.5 M01 Functional Size (uFP): 0.5 1.0 1.5


1.0 M03 Technical Complexity (L-M-H): 1 0.5 1.0 1.5

0.5 M04 Communication Needs (#Sh): 0.5 1.0 1.5

1.0 M05 Team Size (#Tm): 3.5 0.5 1.0 1.5



0.5 M08 Reliability Requirements (%): 0.5 0.5 1.0 1.5


RD – Requirem ents E licitation:TS – Desig n, Develo pm ent & Un it Tests:

QA – V ERification and V ALidation:38 D a y s17 D a y s

Initial

E stim ation Date:1-Jun-1993Approved

S tatus:

R 1 0ISBSG-24586 F in e E n fo rc e m en t

25 D a y s

M01 M02 M03 M04 M05 M06 M07 M08 M09

H ig h

M e d iu m

Lo w

Application: Estim ation ID Estimation Item Description: Release: Based on: Estim ation Point:CmmiEstimation-MIS

Expert Estim ator: Reviewer: Approver:Initial

Mid

Final

Cost Driver Im pact Profile Cost Driver ID: Cost Driver (unit): Low Medium High0.8 M01 Functional Size (uFP): 203 0.5 1.0 1.5




1.0 M05 Team Size (#Tm): 3.5 0.5 1.0 1.5





RD – Requirem ents E licitation:TS – Design, Development & Unit Tests:

QA – VERification and VALidation:240 Days

62 Days

Final

Estim ation Date:Status:

R 1 0ISBSG-24387 Ban kin g

Approved

154 Days

M01 M02 M03 M04 M05 M06 M07 M08 M09

H igh

M ed ium

Lo w

Application: Estimation ID Estimation Item Description: Release: Based on: Estimation Point:CmmiEstimation-MIS


Mid

Final




0.5 M04 Communication Needs (#Sh): 0.5 0.5 1.0 1.5

1.0 M05 Team Size (#Tm): 3.5 0.5 1.0 1.5


1.5 M07 Availability Requirements (#h): 1.5 0.5 1.0 1.5


1.5 M09 Documentation Requirements (L-M-H): 1.5 0.5 1.0 1.5



Final


R 10ISBSG-18836 Transport & Storage

Approved 3-Jun-2003

240 Days

M01 M02 M03 M04 M05 M06 M07 M08 M09

High

M edium

Low

Application: Estimation ID Estimation Item Description: Release: Based on: Estim ation Point:CmmiEstimation-MIS


Mid

Final

Cost Driver Impact Profile Cost Driver ID: Cost Driver (unit): Low Medium High0.5 M01 Functional Size (uFP): 0.5 1.0 1.5



0.5 M04 Communication Needs (#Sh): 0.5 1.0 1.5

0.5 M05 Team Size (#Tm): 3 0.5 1.0 1.5

0.5 M06 Time Constraints (#day): 0.5 1.0 1.5


0.5 M08 Reliability Requirements (%): 0.5 1.0 1.5



QA – VERification and VALidation:

11 Days

R 1 0ISBSG-17065 F in e En fo rcem e n t Initial

Estim ation Date:1-Jun-1994Approved

Status:

14 Days7 DaysM01 M02 M03 M04 M05 M06 M07 M08 M09

H ig h

M ed iu m

L o w



Mid

Final





0.5 M05 Team Size (#Tm): 1 0.5 1.0 1.5







Final


R 10ISBSG-16833 F ine Enfo rcem en t

Approved 3-Mrz-2003

13 Days

M01 M02 M03 M04 M05 M06 M07 M08 M09

H igh

M ed ium

Lo w

Application: Estimation ID Estim ation Item Description: Release: Based on: Estimation Point:CmmiEstimation-MIS

Expert Estim ator: Reviewer: Approver:Initial

Mid

Final

Cost Driver Im pact Profile Cost Driver ID: Cost Driver (unit): Low Medium High0.6 M01 Functional Size (uFP): 67 0.5 1.0 1.5




1.0 M05 Team Size (#Tm): 3.5 0.5 1.0 1.5





RD – Requirem ents Elicitation:TS – Design, Development & Unit Tests:


Approved 3-Mai-2006

131 Days

R10ISBSG-16764 Telecommunications Final



High

M edium

Low



Mid

Final





0.9 M05 Team Size (#Tm): 3 0.5 1.0 1.5







Approved

61 Days

R 10ISBSG-12053 Te lecomm un ica tions Final



H igh

M ed ium

Low



Mid

Final





0.5 M05 Team Size (#Tm): 1 0.5 1.0 1.5







Approved 3-Sep-2001

42 Days

R10ISBSG-11749 Te lecommunications Final



High

M edium

Low



Mid

Final





1.0 M05 Team Size (#Tm): 3.5 0.5 1.0 1.5






QA – VERification and VALidation:187 Days

31 Days

Final


R 1 0ISBSG-10409 Tele co m m u n ica tion s

Approved

100 Days

M01 M02 M03 M04 M05 M06 M07 M08 M09

H ig h

M e d iu m

L o w


Estimation Stack:

1.4 -7.1 -4.3 -1.4 0.0 1.4 4.3 7.1

Target Deviation: 8 Days (4%)

0 2 5 5 5 2 1

Standard Deviation: 3 Days (2%)Maximum Deviation: 10 Days (5%)

Estimation Trust Index: 5 S igma Very good

Estimation I tems

RD TS QA PI PM RD TS QA PI PM RD TS QA PI PMEstimationI tem_003 R1.4 New Customer Wish Initial 19-Okt 7 9 16 8 4 44 8 L M M L L 40EstimationI tem_001 R1.4 New Admin Method Initial 19-Okt 16 22 34 9 8 88 12 14 28 39 8 6 95 L H M H L 80EstimationI tem_009 R2.0 New Generation Mid 19-Okt 19 24 37 13 5 98 25 L M M H M 95EstimationI tem_007 R2.0 Feasibility Studie Initial 19-Okt M H H M M 170EstimationI tem_004 R1.4 Most Important New Feature Initial 19-Okt 38 54 74 24 18 208 59 M H H H M 213EstimationI tem_010 R2.0 Neue Idee Initial 19-Okt 16 18 21 9 7 71 24 M H L M M 71EstimationI tem_005 R1.3 API for SAP Interface Final 19-Okt 24 39 36 13 9 121 17 18 42 39 19 11 129 H M H L M 118EstimationI tem_006 R1.5 Kino Deployment Planning Initial 19-Okt 7 18 35 20 13 92 14 M M M L H 105EstimationI tem_002 R1.4 New Layout Initial 19-Okt 22 41 18 17 98 19 L M M L H 98

Micro Estimation (Days) Actuals per Phase (Days)

Cost Drivers

Macr

o To

tal

ProjectEstim ation

Last

Chan

ge

OK?

Estim

atio

n Po

int

Feature Request / Project Name

Relea

se

Over

all

Estim

atio

n

Risk

Ex

posu

re

Over

all

Actu

als

N ew E stI Remov e C alibrate S plit?Detail

0

1

2

3

4

5

6

-7 -4 -1 0 1 4 7

+ Deviation- Deviation

No. o

f Esti

matio

ns

RestoreRename

Estimation Stack:

1.4 -7.1 -4.3 -1.4 0.0 1.4 4.3 7.1


0 2 5 5 5 2 1


Estimation Trust Index: 5 S ig ma Very good

Estimation I tems



Cost Drivers

Macr

o To

tal

Pro jectEstim ation

Last

Chan

ge

OK?

Estim

atio

n Po

int


Relea

se

Over

all

Estim

atio

n

Risk

Ex

posu

re

Over

all

Actu

als


0

1

2

3

4

5

6

-7 -4 -1 0 1 4 7


No. o

f Esti

matio

ns

RestoreRename

Elimination of Elimination of VarianceVariance The smaller the bell curve base

gets the better the estimation It’s easy to detect bad estimations

The smaller the bell curve base gets the better the estimation

It’s easy to detect bad estimations

VarianceVarianceEstimationEstimation

Tolerance RangeTolerance Range


Estimation Stack:

1.4 -7.1 -4.3 -1.4 0.0 1.4 4.3 7.1


0 2 5 5 5 2 1


Estimation Trust Index: 5 S igma Very good

Estimation I tems



Cost Drivers

Macr

o To

tal

ProjectEstim ation

Last

Chan

ge

OK?

Estim

atio

n Po

int


Relea

se

Over

all

Estim

atio

n

Risk

Ex

posu

re

Over

all

Actu

als


0

1

2

3

4

5

6

-7 -4 -1 0 1 4 7


No. o

f Esti

matio

ns

RestoreRename

Estimation Stack:

1.4 -7.1 -4.3 -1.4 0.0 1.4 4.3 7.1


0 2 5 5 5 2 1


Estimation Trust Index: 5 S ig ma Very good

Estimation I tems



Cost Drivers

Macr

o To

tal

Pro jectEstim ation

Last

Chan

ge

OK?

Estim

atio

n Po

int


Relea

se

Over

all

Estim

atio

n

Risk

Ex

posu

re

Over

all

Actu

als


0

1

2

3

4

5

6

-7 -4 -1 0 1 4 7


No. o

f Esti

matio

ns

RestoreRename

Elimination of Elimination of VarianceVariance The smaller the bell curve base

gets the better the estimation It’s easy to detect bad

estimations

The smaller the bell curve base gets the better the estimation

It’s easy to detect bad estimations

VarianceVariance

Tolerance RangeTolerance Range

EstimationsEstimations


ResultsResults

You see how much every Cost Driver contributes to overall cost Based on the “a”–Parameters that describe

the exponential slope of the Cost Driver

You see how much every Cost Driver contributes to overall cost Based on the “a”–Parameters that describe

the exponential slope of the Cost Driver The "a " Parameters RD TS QA PI PM

Functional Size 0.652 0.359 0.007 0.000 0.000Requirements Volatility 0.368 0.663 0.331 0.489 0.870

Technical Complexity 0.468 1.124 1.204 0.861 0.198Communication Needs 0.913 0.289 0.587 0.100 0.062

Team Size 0.326 0.574 1.177 1.034 1.065

The Targets --> Min imum! 6.3 12.4 17.6 2.8 6.2

No. of Estimations 7 8 8 8 8


AgendaAgenda




Example 1: ISBSG MIS ProjectsExample 1: ISBSG MIS Projects

Data from R10 Repository Release Functional Size by IFPUG uFP Cost Driver Parameters guessed from

environmental information

Data from R10 Repository Release Functional Size by IFPUG uFP Cost Driver Parameters guessed from

environmental information


Calibration of MIS Projects in Calibration of MIS Projects in ISBSG R10 RepositoryISBSG R10 Repository

Estimation Stack: Last Calibration:

16-Nov-2009 12:06

# ### # -10.7 -6.4 -2.1 2.1 6.4 10.7

Target Deviation: 15 Days (0% from estimation)

##### 3 4 6 11 6 6 2

Standard Deviation: 6 Days (0% from estimation)Average Deviation: 0 Days (-0% from estimation)

Maximum Deviation: 14 Days (0% from estimation)Excellent Trust Index: 6 S igma (100% Success Rate)

Estimation ID Estimation I tem Description:

RD TS QA PI PM Risk RD TS QA PI PM M01 M02 M03 M04 M05 M06 M07 M08 M09IS BS G-17065 R10 Fine Enforcement Initial 16-Nov 21 4 1 4 30 90% 21 4 1 4 30 90% 37 +2.1 L L L L L L L L L FALSE

IS BS G-26686 R10 Fine Enforcement Initial 16-Nov 23 3 1 2 29 81% 23 3 1 2 29 81% 42 +2.1 L L M L L L L L L FALSE

IS BS G-24586 R10 Fine Enforcement Initial 16-Nov 44 11 2 11 5 74 87% 25 36 16 11 8 97 99% 95 +3.9 L M M L M L M L L FALSE

IS BS G-28053 R10 Telecommunications Final 16-Nov 50 45 78 12 34 219 99% 60 99 39 8 12 218 100% 216 +13.2 L M H H M L M L M FALSE

IS BS G-32316 R10 Telecommunications Final 16-Nov 28 38 35 5 12 118 90% 37 42 34 5 9 126 94% 143 +7.8 L H L L M L M M L FALSE

IS BS G-29742 R10 Banking Final 16-Nov 22 62 35 5 12 136 92% 34 48 18 10 5 114 99% 115 +6.5 L M L M L M L M L FALSE

IS BS G-11749 R10 Telecommunications Final 16-Nov 32 98 24 3 23 180 99% 36 85 31 5 19 175 98% 184 +6.3 L L M L L M H M L FALSE

IS BS G-16833 R10 Fine Enforcement Final 16-Nov 10 25 15 2 5 57 95% 10 30 12 2 5 58 95% 52 +1.6 L M L L L L L L L FALSE

IS BS G-27658 R10 Telecommunications Final 16-Nov 45 85 30 12 23 195 96% 45 82 28 21 12 188 94% 212 +11.5 L M H M M L M M L FALSE

IS BS G-24387 R10 Banking Final 16-Nov 88 180 80 24 50 422 93% 149 231 51 24 20 475 99% 482 +31.5 M H L M M H M M M FALSE

IS BS G-26661 R10 Case Management Final 16-Nov 123 345 270 123 12 873 99% 234 405 142 63 55 898 99% 886 +47.7 H M H M M H M M M FALSE

IS BS G-16764 R10 Telecommunications Final 16-Nov 45 110 96 7 45 303 79% 137 226 53 30 22 467 99% 459 +24.9 L M L L M M H H M FALSE

IS BS G-18836 R10 Transport & Storage Final 16-Nov 110 360 120 50 60 700 95% 209 340 97 65 77 789 99% 773 +40.0 H L M L M M H M H FALSE

IS BS G-28180 R10 Banking Final 16-Nov 950 2100 450 180 23 3703 93% 1265 2273 371 201 125 4235 100% 4235 +309.7 H H H H H H H H H FALSE

IS BS G-10409 R10 Telecommunications Final 16-Nov 89 114 98 10 13 324 98% 89 144 41 26 12 312 100% 309 +16.2 L M M M M H M M L FALSE

IS BS G-12053 R10 Telecommunications Final 16-Nov 56 89 20 5 5 175 95% 66 74 32 20 12 205 97% 194 +11.0 M H M M M M L M L FALSE

IS BS G-31085 R10 Engineering Final 16-Nov 3 12 6 21 5 47 82% 15 22 12 7 8 64 97% 68 +2.4 L L M L L L L M L FALSE

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CmmiEstimation-MIS

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0

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-11 -6 -2 2 6 11+ Deviation- Deviation

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16-Nov-2009 12:06

## ## # -10.7 -6.4 -2.1 2.1 6.4 10.7


##### 3 4 6 11 6 6 2



Estimation ID Estimation I tem Description:

RD TS QA PI PM Risk RD TS QA PI PM M01 M02 M03 M04 M05 M06 M07 M08 M09IS BS G-17065 R10 Fine Enforcement Initial 16-Nov 21 4 1 4 30 90% 21 4 1 4 30 90% 37 +2.1 L L L L L L L L L FALSE

IS BS G-26686 R10 Fine Enforcement Initial 16-Nov 23 3 1 2 29 81% 23 3 1 2 29 81% 42 +2.1 L L M L L L L L L FALSE

IS BS G-24586 R10 Fine Enforcement Initial 16-Nov 44 11 2 11 5 74 87% 25 36 16 11 8 97 99% 95 +3.9 L M M L M L M L L FALSE

IS BS G-28053 R10 Telecommunications Final 16-Nov 50 45 78 12 34 219 99% 60 99 39 8 12 218 100% 216 +13.2 L M H H M L M L M FALSE

IS BS G-32316 R10 Telecommunications Final 16-Nov 28 38 35 5 12 118 90% 37 42 34 5 9 126 94% 143 +7.8 L H L L M L M M L FALSE

IS BS G-29742 R10 Banking Final 16-Nov 22 62 35 5 12 136 92% 34 48 18 10 5 114 99% 115 +6.5 L M L M L M L M L FALSE

IS BS G-11749 R10 Telecommunications Final 16-Nov 32 98 24 3 23 180 99% 36 85 31 5 19 175 98% 184 +6.3 L L M L L M H M L FALSE

IS BS G-16833 R10 Fine Enforcement Final 16-Nov 10 25 15 2 5 57 95% 10 30 12 2 5 58 95% 52 +1.6 L M L L L L L L L FALSE

IS BS G-27658 R10 Telecommunications Final 16-Nov 45 85 30 12 23 195 96% 45 82 28 21 12 188 94% 212 +11.5 L M H M M L M M L FALSE

IS BS G-24387 R10 Banking Final 16-Nov 88 180 80 24 50 422 93% 149 231 51 24 20 475 99% 482 +31.5 M H L M M H M M M FALSE

IS BS G-26661 R10 Case Management Final 16-Nov 123 345 270 123 12 873 99% 234 405 142 63 55 898 99% 886 +47.7 H M H M M H M M M FALSE

IS BS G-16764 R10 Telecommunications Final 16-Nov 45 110 96 7 45 303 79% 137 226 53 30 22 467 99% 459 +24.9 L M L L M M H H M FALSE

IS BS G-18836 R10 Transport & Storage Final 16-Nov 110 360 120 50 60 700 95% 209 340 97 65 77 789 99% 773 +40.0 H L M L M M H M H FALSE

IS BS G-28180 R10 Banking Final 16-Nov 950 2100 450 180 23 3703 93% 1265 2273 371 201 125 4235 100% 4235 +309.7 H H H H H H H H H FALSE

IS BS G-10409 R10 Telecommunications Final 16-Nov 89 114 98 10 13 324 98% 89 144 41 26 12 312 100% 309 +16.2 L M M M M H M M L FALSE

IS BS G-12053 R10 Telecommunications Final 16-Nov 56 89 20 5 5 175 95% 66 74 32 20 12 205 97% 194 +11.0 M H M M M M L M L FALSE

IS BS G-31085 R10 Engineering Final 16-Nov 3 12 6 21 5 47 82% 15 22 12 7 8 64 97% 68 +2.4 L L M L L L L M L FALSE

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Get out VariationsGet out Variationswith Eigenvector Theory!with Eigenvector Theory!

After calibrating with eight Cost Drivers: Actual vs. Calculated

After calibrating with eight Cost Drivers: Actual vs. Calculated

In ISBSG R10(Selection of MIS) uFP vs. Total Effort

In ISBSG R10(Selection of MIS) uFP vs. Total Effort

0.0

100.0

200.0

300.0

400.0

500.0

600.0

0 100 200 300 400 500 600 700 800 9000.0

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0.0 100.0 200.0 300.0 400.0 500.0 600.0 700.0 800.0 900.0 1000.0


Example 2: GMC’s NFR ProjectsExample 2: GMC’s NFR Projects

NFR = New Feature Requests Every NFR is an Estimation Item for the

new product release Cost Drivers remain very stable Disregard them!

Size Drivers describe impact of NFR for each module Predicts effort needed for new release

NFR = New Feature Requests Every NFR is an Estimation Item for the

new product release Cost Drivers remain very stable Disregard them!

Size Drivers describe impact of NFR for each module Predicts effort needed for new release


Estimation Stack for GMC’s Estimation Stack for GMC’s PrintNet Design Center NFRsPrintNet Design Center NFRs


16-Nov-2009 13:09

##### -3.6 -2.1 -0.7 0.7 2.1 3.6


##### 0 3 7 35 6 1 0



Request I D Request Description:

Dev QA Doc Risk Dev QA Doc M01 M02 M03 M04 M05 M06 M07 M08 M09 M10 M11 M12 M13NFR10015-1 Rel. 5.3 Client Specific document archieving within Web Proof IIFinal 16-Nov 3 3 1 1 1 L FALSE

NFR10035-2 Rel. 5.3 Complete Print function - Flash Final 16-Nov 6 6 5 5 9 M FALSE

NFR10035-1 Rel. 5.3 Complete Print function - HTML Final 16-Nov 3 3 10 10 9 L L M FALSE

NFR10114-1 Rel. 5.3 Flash - Data Viewing - Advanced Final 16-Nov 4 4 FALSE

NFR10271-1 Rel. 5.3 Save PNI paragraph in xml WFD format Final 16-Nov 5 5 13 13 13 M FALSE

NFR10272-1 Rel. 5.3 WebDAV support for Connect Final 16-Nov 14 14 15 15 12 H H H H M H FALSE

NFR10273-2 Rel. 5.3 New simple GUI for end user like secretaryFinal 16-Nov 11 11 18 18 19 M H M H FALSE

NFR10276-1 Rel. 5.3 Users definition on which is possible transfer document for signFinal 16-Nov 5 5 2 2 2 H L FALSE

NFR10277-1 Rel. 5.3 Compare with older version in paragraph editing pageFinal 16-Nov 5 5 3 3 2 M FALSE

NFR10278-1 Rel. 5.3 Add publish/reject buttons to paragraph editing pageFinal 16-Nov 3 3 2 2 2 M L FALSE

NFR10279-1 Rel. 5.3 new PNI variable of WFD type Final 16-Nov 5 5 5 5 5 M M FALSE

NFR10288-1 Rel. 5.3 Action Plugin to call PrintNetT wfd Final 16-Nov 3 3 2 2 1 H FALSE

NFR10360-1 Rel. 5.3 Paragraph Condition Management Final 16-Nov 25 25 17 17 16 M H H M M H H M H M FALSE

NFR10361-1 Rel. 5.3 Add caption or description field to a paragraph in PNI at the creation timeFinal 16-Nov 5 5 3 3 2 L L FALSE

PNI -070002 Date picker in form control Final 16-Nov 5 5 1 1 3 L M L FALSE

PNI -070004 Letter description Final 16-Nov 10 10 1 1 2 6 M M M FALSE

PNI -070005 User profiles from WebCenter Final 16-Nov 3 3 2 3 5 3 L L FALSE

PNI -070008 Handling all data types (csv, flat) Final 16-Nov 3 3 4 2 6 8 M M M FALSE

PNI -070009 Campaign Management Final 16-Nov 19 19 14 14 13 M H L FALSE

PNI -070010 Document Production Improvements Final 16-Nov 7 7 29 29 28 H H M M H FALSE

PNI -070011 Move and rename assets in PNConnect Final 16-Nov 3 3 5 1 6 8 L H M FALSE

PNI -070012 Flash based Proofing component for WebProofFinal 16-Nov 57 57 26 26 25 H FALSE

PNI -070013 Press sence iWay integration Final 16-Nov 15 15 12 12 13 M M FALSE

PNI -070016 PNI & PNC: List of logged users Final 16-Nov 5 5 3 3 3 M L M FALSE

NFR10055-1 Rel. 5.3 Highlighted variables Final 16-Nov 5 5 6 6 6 L L L FALSE

PNI -070003 Second Signature Scenario (with mail notification)Final 16-Nov 2 2 2 1 3 3 M FALSE

Module I mpactExpert Estimation (Days)

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10152025303540


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##### -3.6 -2.1 -0.7 0.7 2.1 3.6


##### 0 3 7 35 6 1 0



Request I D Request Description:

Dev QA Doc Risk Dev QA Doc M01 M02 M03 M04 M05 M06 M07 M08 M09 M10 M11 M12 M13NFR10015-1 Rel. 5.3 Client Specific document archieving within Web Proof IIFinal 16-Nov 3 3 1 1 1 L FALSE

NFR10035-2 Rel. 5.3 Complete Print function - Flash Final 16-Nov 6 6 5 5 9 M FALSE

NFR10035-1 Rel. 5.3 Complete Print function - HTML Final 16-Nov 3 3 10 10 9 L L M FALSE

NFR10114-1 Rel. 5.3 Flash - Data Viewing - Advanced Final 16-Nov 4 4 FALSE

NFR10271-1 Rel. 5.3 Save PNI paragraph in xml WFD format Final 16-Nov 5 5 13 13 13 M FALSE

NFR10272-1 Rel. 5.3 WebDAV support for Connect Final 16-Nov 14 14 15 15 12 H H H H M H FALSE

NFR10273-2 Rel. 5.3 New simple GUI for end user like secretaryFinal 16-Nov 11 11 18 18 19 M H M H FALSE

NFR10276-1 Rel. 5.3 Users definition on which is possible transfer document for signFinal 16-Nov 5 5 2 2 2 H L FALSE

NFR10277-1 Rel. 5.3 Compare with older version in paragraph editing pageFinal 16-Nov 5 5 3 3 2 M FALSE

NFR10278-1 Rel. 5.3 Add publish/reject buttons to paragraph editing pageFinal 16-Nov 3 3 2 2 2 M L FALSE

NFR10279-1 Rel. 5.3 new PNI variable of WFD type Final 16-Nov 5 5 5 5 5 M M FALSE

NFR10288-1 Rel. 5.3 Action Plugin to call PrintNetT wfd Final 16-Nov 3 3 2 2 1 H FALSE

NFR10360-1 Rel. 5.3 Paragraph Condition Management Final 16-Nov 25 25 17 17 16 M H H M M H H M H M FALSE

NFR10361-1 Rel. 5.3 Add caption or description field to a paragraph in PNI at the creation timeFinal 16-Nov 5 5 3 3 2 L L FALSE

PNI -070002 Date picker in form control Final 16-Nov 5 5 1 1 3 L M L FALSE

PNI -070004 Letter description Final 16-Nov 10 10 1 1 2 6 M M M FALSE

PNI -070005 User profiles from WebCenter Final 16-Nov 3 3 2 3 5 3 L L FALSE

PNI -070008 Handling all data types (csv, flat) Final 16-Nov 3 3 4 2 6 8 M M M FALSE

PNI -070009 Campaign Management Final 16-Nov 19 19 14 14 13 M H L FALSE

PNI -070010 Document Production Improvements Final 16-Nov 7 7 29 29 28 H H M M H FALSE

PNI -070011 Move and rename assets in PNConnect Final 16-Nov 3 3 5 1 6 8 L H M FALSE

PNI -070012 Flash based Proofing component for WebProofFinal 16-Nov 57 57 26 26 25 H FALSE

PNI -070013 Press sence iWay integration Final 16-Nov 15 15 12 12 13 M M FALSE

PNI -070016 PNI & PNC: List of logged users Final 16-Nov 5 5 3 3 3 M L M FALSE

NFR10055-1 Rel. 5.3 Highlighted variables Final 16-Nov 5 5 6 6 6 L L L FALSE

PNI -070003 Second Signature Scenario (with mail notification)Final 16-Nov 2 2 2 1 3 3 M FALSE

Module I mpactExpert Estimation (Days)

NFR-Estimations_PNIRe

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Project 3: COSMIC or IFPUG?Project 3: COSMIC or IFPUG?

First Sizing – Initial Requirements Quick and Early Functional Sizing based on Initial User

Requirements Take IFPUG 4.3 “Unadjusted”

Second Sizing – Technical Requirements Size final User Requirements after Analysis Once with IFPUG 4.3 “Unadjusted” Then size Use Cases with COSMIC 3.0

Two Size Drivers Add Cost Drivers as needed

First Sizing – Initial Requirements Quick and Early Functional Sizing based on Initial User

Requirements Take IFPUG 4.3 “Unadjusted”

Second Sizing – Technical Requirements Size final User Requirements after Analysis Once with IFPUG 4.3 “Unadjusted” Then size Use Cases with COSMIC 3.0

Two Size Drivers Add Cost Drivers as needed


AgendaAgenda




Benefits of Six SigmaBenefits of Six Sigma

Six Sigma Eigenvector Theory tells you How to calculate the “a”–Parameters Which measured Cost Drivers affect you most How to improve your project estimation practice

Six Sigma Eigenvector Theory tells you How to calculate the “a”–Parameters Which measured Cost Drivers affect you most How to improve your project estimation practice


ConclusionConclusion

Six Sigma effectively predicts defect density Requires sophisticated measurement program And sound mathematical statistics!

Don’t underestimate impact of early stages Voice of the Customer Requirements Elicitation

You must measure Functional Size! Time is of Essence!

Six Sigma effectively predicts defect density Requires sophisticated measurement program And sound mathematical statistics!

Don’t underestimate impact of early stages Voice of the Customer Requirements Elicitation

You must measure Functional Size! Time is of Essence!


Thank You.

Software Project Estimation - Euro Project Office

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