integration of software cost estimates across cocomo, seer-sem, and price-s models

Integration of Software Cost Estimates Across COCOMO, SEER-SEM, and PRICE-S models

Integration of Software Cost Estimates Across COCOMO, SEER-SEM, and PRICE-S models

Tom Harwick, Engineering SpecialistNorthrop Grumman CorporationIntegrated SystemsOctober 26-29, 2004

19th International Forum on COCOMO and Software Cost Modeling


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Outline

• Need for Inter-Model Estimate Integration

• Model Analysis– Model Comparisons & Sensitivity Analysis– Economic Properties

• Baseline Example– COCOMO– SEER-SEM– PRICE-S

• Summary



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Need for Inter-Model Estimate Integration

• Communication of overall Software Cost Estimate to the Customer

• Minimize Program Cost Risk

• Integration of Software Estimates Across Vendors– Various Software Cost Models

– Sizing Definitions– Selection of Cost Driver Settings

– Vendors have different Statement Of Work (SOW)



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Model Analysis

• Approach– Understand Properties of Each Cost Model

– Sizing and Sizing Normalization– Productivity Cost Drivers

–Team & Process–Market – Customer(s)–Complexity, Operating Environment–Model Scope Differences



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Model Analysis - Cost Drivers by Cost Model

Team & Process COCOMO II SEER-SEM PRICE-S Team ACAP, PCAP, APEX,

PLEX, LTEX Analyst Capabilities; Analyst Experience;

Programmer Capabilities;

Programmers Language Experience

INTEGI

Process TOOL, PCON, PVOL Development Method – KnowledgeBase; and

Practices and Methods Experience

CPLX1

Market/ Customer COCOMO II SEER-SEM PRICE-S Market RUSE, SITE, SECU * Requirements

Definition Normality ; Development Method –

Application and Acquisition Method-

KnowledgeBase; Multiple Site Development

CPLXM, CPLX1

Schedule SCED Required Schedule; Start Date

DSTART, PEND

Reliability RELY Certification Requirements

DOCU Development Standard - KnowledgeBase

Standard

Complexity COCOMO II SEER-SEM PRICE-S Complexity CPLX, DATA, STOR Application –

KnowledgeBase UTIL, APPL

Operating Environment & Technology

TIME Platform; and DevelopmentMethod -

KnowledgeBase

PLTFM

Sizing & Exponent COCOMO II SEER-SEM PRICE-S Size Raw KSLOC New Lines of Code;

Pre-existing SLOC. Raw SLOC

Normalized size Equiv. new KSLOC New Lines of Code NEWD, NEWD, SLOC

Exponent PREC, FLEX, RESL,

TEAM, PMAT- or SEI rating

Scope COCOMO II SEER-SEM PRICE-S Hardware dev. in parallel with software

Not in the default model CPLX2

System Integration



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Model Analysis - Concept of “Ceterus Parabus”

Methodology to Extract Cost Driver Information

Parameter(e.g. Analyst Experience)

Cost

Other parametersheld constant

Slope = delta Cost/delta (Analyst Experience)

Range = Cost/(Analyst Experience)



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Model Analysis - Major COCOMO Cost Drivers

COCOMO II - Local Impact (about Baseline, +/- 1 setting)

- 0.20 0.40 0.60 0.80 1.00 1.20 1.40 1.60

STOR=Nom

SECU = High

SCED= Nom

RELY= VHigh

PLEX= Nom

LTEX= Nom

TOOL=Nom

DOCU= Nom

SITE= Low

APEX = High

PCON= Nom

TIME= XHigh

DATA= High

CPLX= XHigh

RUSE= Nom

PCAP= Nom

PVOL= Nom

ACAP = High

Environmental Factors



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Model Analysis - Major SEER-SEM Cost Drivers

SEER-SEM 7.0

0% 10% 20% 30% 40% 50% 60% 70% 80%

Specif ication Level Reliability= “Hi”

LanguageType = “Nom”

Programmers Language Experience = “Nom”

Multiple Site Development= “VHi”

Process Volatility= “Nom+”

Automated Tool Use = “Nom+”

Modern Development Practices Use = “Hi-“

Requirements Volatility= “Hi”

Programmer Capabilities = “Nom”

Analyst Capabilities = “Nom+”

Analyst Applic. Experience = “Nom”

Security Requirements= “Nom+”

Local Impact, Percentage



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Model Analysis - Major PRICE-S Cost Drivers

PRICE-S - Local Range

- 0.50 1.00 1.50 2.00

CPLX1-Tools= -0.1, CPLX1-Req.+new Lang. = 0

CPLX1 -product familiarity

PLTFM = 1.8

CPLXM=1.2

PROFAC= 6.5

INTEGI-timing= 0.7,INTEGE-timing=0.7,UTIL=0.5,APPL=8.46

INTEGI-Team=0.70, INTEGE-Team= 0.70, CPLX1-person.= -

0.2

Productivity Factors



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Model Analysis - Team & ProcessTeam & Process

COCOMO II SEER-SEM PRICE-S

Team

ACAP (1.41)

Analyst Capabilities (1.20)

INTEGI (crew) (1.58)

PCAP (1.31) Programmer Capabilities (1.18)

PROFAC (1.35)

APEX (1.23)

Analyst Experience (1.21)

CPLX1 – Product familiarity (1.25)

PLEX (1.20)

LTEX (1.20)

Programmers Language Experience (1.05)

Process & Tools

PVOL (1.99)

Development Method –

KnowledgeBase;

CPLX1 (1.10)

PCON (1.24)

Process & Reqmts. Volatility (1.32)

CPLX!-Reqmts. Volatility (1.13)

TOOL (1.21)

Practices and Methods Experience

CPLX1-Tools (1.23)



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Model Analysis - Market

Market COCOMO II SEER-SEM PRICE-S Market RUSE (1.31) SITE (1.22) CPLXM (1.2) SECU (1.10) Security

Requirements(1.75)

Schedule SCED (1.14) Required Schedule;

Start Date DSTART, PEND

Reliability RELY (1.15) Requirements

Definition Formality; (part of Platform,

Application -Kbase)

(part of PLTFM)

Certification requirements

DOCU (1.22) Development Standard -

Kbase



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Model Analysis - Complexity

Complexity COCOMO II SEER-SEM PRICE-S Complexity CPLX (1.30)

Application (Kbase) APPL

DATA (1.28) UTIL STOR (1.05) INTEGI (Timing.

Coupling) (1.55) Operating Environment & Technology

TIME (1.26) Platform; and DevelopmentMethod -

KnowledgeBase

PLTFM (1.25)



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Model Analysis - Scope

Scope b/t Models COCOMO II SEER-SEM PRICE-S

Hardware developed in parallel w/ software

Not included Hardware Integration Level

(1.09)

CPLX2 (1.20)

System Integration

Not included

Programs Currently

Integrating (1.04)

INTEGE (1.13)

Did not find many data points in the COCOMO I database that suggest a System Integration on the scale of our (next) Baseline example (3-4 Million SLOCs).

Has COCOMO II been able to collect sufficient data to develop a (large scale) System Integration factor?



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Model Analysis - Economic Properties(Models Exponents Indicate Diseconomies of Scale for Large Projects)

COCOMO, SEER-SEM, PRICE-S

-

2,000

4,000

6,000

8,000

10,000

12,000

14,000

16,000

18,000

20,000

0 100 200 300 400 500 600 KSLOC

Eff

ort

- P

ers

on

-Mo

nth

s

COCOMO

SEER-SEM

PRICE-S

y = 19.4 x 1.09

y = 15.1 x 1.12

y = 9.7 x 1.11

The COCOMO II exponent ranges from 0.91 to approximately 1.26 depending upon the Scale Factors.



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Model Analysis - Economic Properties

Schedule Variation (SLOC = 50,000)

-

500

1,000

1,500

2,000

2,500

0 20 40 60 80

Months

COCOMO

PRICE-S

SEER-SEM

All Software Cost Models show a Schedule Compression penalty.



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Baseline Example

• Illustrate Software Cost Estimation & Integration– Baseline

– SEER-SEM – Sensor Software– PRICE-S – Air Vehicle Software– COCOMO – Control Station

– Sizing Assumptions and Scenarios– Equivalent New SLOC

– Productivity Range– Hours/ SLOC

– Estimating Ranges using Monte Carlo– Hours = (SLOC) X (Hours/SLOC)



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Baseline Example - Sizing Scenarios (Equivalent New SLOC)

Air Vehicle

Code growth of -5%, 15% reuse

Code growth of 15%, 10% reuse

Code growth of 20%, 5% Reuse

Original SLOC point estimate SLOC-Low SLOC-Most Likely SLOC-High 2,000,000 1,615,000 2,070,000 2,280,000

Sensor



Code growth of 30%, no Reuse

Original SLOC point estimate SLOC-Low SLOC-Most Likely SLOC-High 500,000 380,000 517,500 650,000

Ground Control Station



Code growth of 20%, 15% Reuse

Original SLOC point estimate SLOC-Low SLOC-Most Likely SLOC-High 1,000,000 475,000 825,000 1,020,000 19th International Forum on COCOMO and Software Cost Modeling


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Baseline Example – Productivity Ranges- Productivity (Hours/SLOC)

PRODUCTIVITY (Hours/SLOC) Model

High Productivity

Medium Productivity Low Productivity

Air Vehicle PRICE-S 3.66

4.48 5.74 Sensor SEER-SEM 4.02 4.91 6.29 Ground Control Station COCOMO 3.32 4.06 5.20

COCOMO Assumptions:

COCOMO – Normalized to include Security, HW/SW integration, System Integration. This adds approximately 50% to the raw COCOMO II estimate. The EAF = 3.32 for the Gnd. Cntl. Stn. The Equivalent New SLOC = 825,000. The assumed module level was 50,000 SLOC. The internal integration = 1.05; the external integration = 1.11 for the “Medium Productivity” (COCOMO) setting.



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Baseline Example – Results

Software - Total Effort50% Occurs at 15.1 Million Hours

0102030405060708090

100

- 5.00 10.00 15.00 20.00 25.00

Hours (Millions)

Pro

bab

ilit

y



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Baseline Example – Air Vehicle, Hours = (Hours/SLOC) X SLOC

Probability SLOC, Thousands Hours/SLOC Hours, in Millions 0 1625 3.70 6.14

10 1794 4.07 7.83 20 1864 4.24 8.28 30 1914 4.37 8.59 40 1960 4.48 8.87 50 2002 4.61 9.16 60 2038 4.73 9.44 70 2071 4.87 9.76 80 2110 5.03 10.13 90 2164 5.22 10.64

100 2275 5.73 12.48



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Baseline Example – Sensor, Hours = (Hours/SLOC) X SLOC

Probability SLOC, Thousands Hours/SLOC Hours, in Millions 0 382 4.03 1.66

10 441 4.48 2.15 20 467 4.66 2.30 30 486 4.81 2.42 40 502 4.93 2.52 50 518 5.05 2.61 60 531 5.18 2.71 70 548 5.33 2.82 80 566 5.52 2.94 90 590 5.74 3.13

100 647 6.28 3.99



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Baseline Example–Control Station,Hours=(Hours/SLOC)X SLOC

Probability SLOC, Thousands Hours/SLOC Hours, in Millions 0 478.8 3.33 1.73

10 608.7 3.69 2.50 20 669.1 3.86 2.75 30 715.4 3.97 2.95 40 753.4 4.07 3.10 50 786.1 4.17 3.25 60 815.8 4.28 3.40 70 845.5 4.40 3.57 80 878.1 4.55 3.75 90 923.3 4.73 3.98

100 1017.1 5.18 5.06



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Summary

• Identified Major “local domain” Cost Drivers– COCOMO II, SEER-SEM, PRICE-S

• Examined Economic Properties– Diseconomies of Scale, Schedule Compression– Productivity Ranges

– Scenario Analysis using top 2-3 Cost Drivers

• Illustrated Software Cost Estimation & Integration– Model Integration using Monte Carlo Probability

Curves– Scenarios for Sizing– Model Output for Productivity



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Object Oriented Analysis (OOA)

• OOA– Unified Modeling Language (UML)

– Several Standard Diagrams– Starts with “Use Case” (users view of the world)– Develops charts including:

– Use Case (Actors use of information in the system)– Collaboration Diagrams (provide details of each Use Case)– Class Diagram

– Specify Blueprints (for objects) – Attributes and Methods– Relationship between Classes

– Objects are linked across a design– Object Interfaces

– Encapsulates Local Variables within a “Black Box”– Inheritance of Classes



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Snapshot of Unified Modeling Language (UML) – From Use Case, Flow of Information, to Class Diagrams of Systems

May 9, 2004 UML Final, Harwick

Medical System – UML Use Case

Login in

Make Appt

Approve PrescriptionCancel Appt

Cancel Appt

Change Doctor

Change Location

Request Prescription

View Patient History



Patient

Nurse

Doctor

Receive notification

Receive RefillRequest

Receive co-pay

Receive Authorization

Pharmacist

Provideco-pay

Insurance

Requestco-pay

Calc co-pay


Collaboration Diagram- Make Appointment

:patient

:scheduler

:Insur_Obj.

:Controller

1: request appt.

Assume successful login.

2: request Insur. Co-Pay

3: send Insur. Co-Pay Amount

4: request schedule availability

5: display schedule availability

:Appt._Object

6: book Appt.

7: confirm Appt.

6: Display/save Appt. results w co-Pay info. on patient computer


Medical System UML Class Diagram –Top View

Appointment

Messaging

Overview - Big Picture

Person

Doctor Patient

Location

Insur.

Nursing

Appoint. Medical Record

Prescription


Medical System UML Class Diagram–Appointment View

Doctor Nursing Staff

Dept.; Location

View_Med_Hist()Write_Prescript.()Renew_Prescript.()Cancel_Apt *Update_Med_Hist()

PersonName; Age;Address; IDView_Med_Hist()Cancel_Apt()

Dept; Location

View_Med_Hist()Cancel_Apt *

Link to Prescription Package

Link to Messaging Package

Patient

Make_Apt()Chng_Doc()Chng_Loc()Req_Prescript.()Request_CoPay()Cancel_Apt() *

Primary_Doctor_Name

* An Override method in the subclasses.



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Object Oriented Analysis (OOA) Benefits• Object Oriented Analysis with Unified Modeling Language

– Overall Project Cost Savings– Similar to System Engineering impact:

– Mitigate Risk, Cost, Schedule Impacts– Decreases Defects

– May increase Design work– But Avoids excessive re-work

– Facilitates Integrate and Test – Better designed Interfaces

– Promotes System Integration – If compatible UML used on all vendor designs

– Helps Customer Understand Product Purchased– Sooner, Decrease Requirements Volatility

– Need Data to Measure impact of OOA (UML)19th International Forum on COCOMO and Software Cost Modeling


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References• Models:• Price Systems, PRICE-S model, Los Angeles, California,

1996.• Galorath Associates, SEER-SEM, Los Angeles, California,

1996.• USC Associates, COCOMO II.1999, Los Angeles, California,

2004.

• Books:• 1. Boehm, Reifer, et al., Software Cost Estimation with

COCOMO II, Prentice-Hall, New Jersey, 2000. • 2. Boehm, Barry W., Software Engineering Economics,

Prentice-Hall, New Jersey, 1981.• 3. Deming, W. Edward, Out of the Crisis, Massachusetts

Institute of Technology, Cambridge, 1982.• 4. Cs3 Inc., Objected Oriented Analysis Using the Unified

Modeling Language, (UML), Los Angeles, CA, 2000-2003.


integration of software cost estimates across cocomo, seer-sem, and price-s models

Documents

software cost modelingchart21

cost modelsizing

new kslocnew lines of

prices modelstom harwick

ceterus parabusmethodology

steam acap

raw slocnormalized sizeequiv