The COCOMO II Suite of Software Cost Estimation Models

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University of Southern CaliforniaCenter for Software EngineeringC S E

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The COCOMO II Suite of Software Cost Estimation Models

Barry Boehm, USCCOCOMO/SSCM Forum 21 Tutorial

November 8, 2006

[email protected], http://csse.usc.edu/research/cocomosuite

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Thanks to USC-CSSE Affiliates (33)• Commercial Industry (10)

– Cost Xpert Group, Galorath, Group Systems, IBM, Intelligent Systems, Microsoft, Motorola, Price Systems, Softstar Systems, Sun

• Aerospace Industry (8)– BAE Systems, Boeing, General Dynamics, Lockheed Martin, Northrop

Grumman(2), Raytheon, SAIC

• Government (6)– FAA, NASA-Ames, NSF, US Army Research Labs, US Army TACOM,

USAF Cost Center

• FFRDC’s and Consortia (6)– Aerospace, FC-MD, IDA, JPL, SEI, SPC

• International (3)– Institute of Software, Chinese Academy of Sciences, EASE (Japan), Samsung

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USC-CSSE Affiliates Program• Provides priorities for, access to USC-CSE research

– Scalable spiral processes, cost/schedule/quality models, requirements groupware, architecting and re-engineering tools, value-based software engineering methods.

– Experience in application in DoD, NASA, industry– Affiliate community events

• 14th Annual Research Review and Executive Workshop, USC Campus, February 12-15, 2007

• 11th Annual Ground Systems Architecture Workshop (with Aerospace Corp.), Manhattan Beach, CA, March 26-29, 2007

• 22nd International COCOMO/Systems and Software Cost Estimation Forum, USC Campus, October 23-26, 2007

• Synergetic with USC distance education programs– MS – Systems Architecting and Engineering– MS / Computer Science – Software Engineering

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Outline• COCOMO II Overview

– Motivation and Context– Model Form and Parameters– Calibration and Accuracy

• Overview of Emerging Extensions– COTS Integration (COCOTS)– Quality: Delivered Defect Density (COQUALMO)– Phase Distributions (COPSEMO)– Rapid Application Development Schedule (CORADMO)– Productivity Improvement (COPROMO)– Product Line Investment (COPLIMO)– System Engineering (COSYSMO)– System of System Integration (COSOSIMO)– COCOMO II Security Extensions (COSECMO)– Network Information Protection (CONIPMO)

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Software product size estimate Software development

mainten ance cost and schedule estimates

Software product, process, com-puter, and personal attributes

Cost, schedule, distribution by

Software reuse, maintenance,and increment parameters

phase, activity, incrementSoftware organization’sProject data

COCOMO recalibrated to organization’s data

COCOMO

COCOMO Baseline Overview I

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1. Introduction 2. Model Definition 3. Application Examples 4. Calibration 5. Emerging Extensions 6. Future Trends Appendices

– Assumptions, Data Forms, User’s Manual, CD Content

COCOMO II Book Table of Contents- Boehm, Abts, Brown, Chulani, Clark, Horowitz, Madachy, Reifer, Steece,

Software Cost Estimation with COCOMO II, Prentice Hall, 2000

CD: Video tutorials, USC COCOMO II.2000, commercial tool demos, manuals, data forms, web site links, Affiliate forms

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Need to ReEngineer COCOMO 81

• New software processes

• New sizing phenomena

• New reuse phenomena

• Need to make decisions based on incomplete information

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Feasibility

Concept ofOperation

Rqts.Spec.

Plansand

Rqts.

ProductDesign

ProductDesignSpec.

DetailDesignSpec.

DetailDesign

Devel.and Test

AcceptedSoftware

Phases and Milestones

RelativeSize Range x

4x

2x

1.25x

1.5x

0.25x

0.5x ApplicationsComposition

(3 parameters)

Early Design(13 parameters)

Post-Architecture(23 parameters)0.67x

0.8x

COCOMO II Model Stages

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Early Design and Post-Architecture Model

FactorsScaleProcessSizeEffort

sMultiplier

Environment

Environment: Product, Platform, People, Project Factors

Size: Nonlinear reuse and volatility effects

Process: Constraint, Risk/Architecture, Team, Maturity Factors

FactorsScaleProcess EffortSchedule Multiplier

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New Scaling Exponent Approach

• Nominal person-months = A*(size)B

• B = 0.91 + 0.01 (scale factor ratings)- B ranges from 0.91 to 1.23- 5 scale factors; 6 rating levels each

• Scale factors:- Precedentedness (PREC)- Development flexibility (FLEX)- Architecture/ risk resolution (RESL)- Team cohesion (TEAM)- Process maturity (PMAT, derived from SEI CMM)

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Project Scale FactorsPM

estimated2.94(Size) B EM

i

B .0.910.01 SFi

Scale Factors(Wi)

Very Low Low Nominal High Very High Extra High

PREC thoroughlyunprecedented

largelyunprecedented

somewhatunprecedented

generallyfamiliar

largely familiar throughlyfamiliar

FLEX rigorous occasionalrelaxation

somerelaxation

generalconformity

someconformity

general goals

RESL little (20%) some (40%) often (60%) generally(75%)

mostly (90%) full (100%)

TEAM very difficultinteractions

some difficultinteractions

basicallycooperativeinteractions

largelycooperative

highlycooperative

seamlessinteractions

PMAT weighted sum of 18 KPA achievement levels

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Relativecost

Amount Modified

1.0

0.75

0.5

0.25

0.25 0.5 0.75 1.0

0.55

0.70

1.0

0.046

Usual LinearAssumption

Data on 2954NASA modules

[Selby, 1988]

Nonlinear Reuse Effects

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Reuse and Reengineering Effects

• Add Assessment & Assimilation increment (AA)- Similar to conversion planning increment

• Add software understanding increment (SU)- To cover nonlinear software understanding effects- Coupled with software unfamiliarity level (UNFM)- Apply only if reused software is modified

• Results in revised Equivalent Source Lines of Code (ESLOC)

- AAF = 0.4(DM) + 0.3 (CM) + 0.3 (IM)- ESLOC = ASLOC[AA+AAF(1+0.02(SU)(UNFM))], AAF < 0.5 - ESLOC = ASLOC[AA+AAF(SU)(UNFM))], AAF > 0.5

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Software Understanding Rating / Increment

Very Low Low Nom High Very High

Structure Very lowcohesion, high

coupling,spaghetti code.

Moderately lowcohesion, high

coupling.

Reasonablywell -

structured;some weak

areas.

High cohesion,low coupling.

Strongmodularity,information

hiding indata/controlstructures.

ApplicationClarity

No matchbetween

program andapplication

world views.

Somecorrelation

betweenprogram andapplication .

Moderatecorrelation


Goodcorrelation


Clear matchbetween

program andapplication

world views.Self-

DescriptivenessObscure code;documentation

missing,obscure orobsolete.

Some codecommentary andheaders; some

usefuldocumentation.

Moderate levelof code

commentary,headers,

documentation.

Good codecommentaryand headers;

usefuldocumentation;

some weakareas.

Self-descriptive

code;documentationup-to-date,

well-organized,with designrationale.

SU Increment toESLOC

50 40 30 20 10

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Other Major COCOMO II Changes

• Range versus point estimates

• Requirements Volatility (Evolution) included in Size

• Multiplicative cost driver changes

- Product CD’s

- Platform CD’s

- Personnel CD’s

- Project CD’s

• Maintenance model includes SU, UNFM factors from reuse model

– Applied to subset of legacy code undergoing change

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Percentage of sample projects within 30% of actuals

-Without and with calibration to data source

COCOMO II Estimation Accuracy:

COCOMO81 COCOMOII.2000COCOMOII.1997

# Projects 63 83 161

Effort

Schedule

81% 52%64%

75%80%

61%62%

72%81%

65%

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COCOMO II. 2000 Productivity Ranges

Productivity Range

1 1.2 1.4 1.6 1.8 2 2.2 2.4

Product Complexity (CPLX)

Analyst Capability (ACAP)

Programmer Capability (PCAP)

Time Constraint (TIME)

Personnel Continuity (PCON)

Required Software Reliability (RELY)

Documentation Match to Life Cycle Needs (DOCU)

Multi-Site Development (SITE)

Applications Experience (AEXP)

Platform Volatility (PVOL)

Use of Software Tools (TOOL)

Storage Constraint (STOR)

Process Maturity (PMAT)

Language and Tools Experience (LTEX)

Required Development Schedule (SCED)

Data Base Size (DATA)

Platform Experience (PEXP)

Architecture and Risk Resolution (RESL)

Precedentedness (PREC)

Develop for Reuse (RUSE)

Team Cohesion (TEAM)

Development Flexibility (FLEX)

Scale Factor Ranges: 10, 100, 1000 KSLOC

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Status of Models

COCOMO II

COCOTS

COQUALMO Defects in Defects out

CORADMO

COSYSMO

Literature BehaviorSignif. Variables Delphi

Data, Bayes

**

****

**

****

**

****

**

**

*

>200

20

6

6

16

60

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COCOMO vs. COCOTS Cost SourcesS

TA

FF

ING

TIME

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COCOMO II

Current COQUALMO System

COQUALMO

DefectIntroduction

Model

DefectRemoval

Model

Software platform, Project, product and personnel attributes

Software Size Estimate

Defect removal profile levelsAutomation, Reviews, Testing

Software development effort, cost and schedule estimate

Number of residual defectsDefect density per unit of size

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Defect Removal Rating Scales

Highly advanced

tools, model-based test

More advance test tools,

preparation.

Dist-monitoring

Well-defined test seq. and

basic test coverage tool

system

Basic test

Test criteria based on checklist

Ad-hoc test and debug

No testingExecution Testing and

Tools

Extensive review

checklist

Statistical control

Root cause analysis,

formal follow

Using historical data

Formal review roles and Well-trained people

and basic checklist

Well-defined preparation,

review, minimal

follow-up

Ad-hoc informal walk-

through

No peer reviewPeer Reviews

Formalized specification, verification.

Advanced dist-

processing

More elaborate req./design

Basic dist-processing

Intermediate-level module

Simple req./design

Compiler extension

Basic req. and design

consistency

Basic compiler capabilities

Simple compiler syntax

checking

Automated Analysis

Extra HighVery HighHighNominalLowVery Low

COCOMO II p.263

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Defect Removal Estimates- Nominal Defect Introduction Rates

60

28.5

14.37.5

3.5 1.60

10

20

30

40

50

60

70

VL Low Nom High VH XH

Delivered Defects/ KSLOC

Composite Defect Removal Rating

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COCOMO II cost drivers

(except SCED)

Language Level,

experience,...

COCOMO II

Phase Distributions(COPSEMO)

RAD Extension

Baseline effort,

schedule

Effort,

schedule by stage

RAD effort, schedule by phase

RVHL

DPRS

CLAB

RESL

COCOMO II RAD Extension (CORADMO)

PPOSRCAP

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0

2

4

6

8

10

12

14

16

0 10 20 30 40 50

PM

M

3.7*(Cube root) 3*(Cube root) Square root

RCAP = XL

RCAP = XH

Effect of RCAP on Cost, Schedule

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COPROMO (Productivity) Model• Uses COCOMO II model and extensions as

assessment framework– Well-calibrated to 161 projects for effort, schedule

– Subset of 106 1990’s projects for current-practice baseline

– Extensions for Rapid Application Development formulated

• Determines impact of technology investments on model parameter settings

• Uses these in models to assess impact of technology investments on cost and schedule– Effort used as a proxy for cost

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The COPLIMO Model– Constructive Product Line Investment Model

• Based on COCOMO II software cost model– Statistically calibrated to 161 projects, representing 18 diverse

organizations

• Based on standard software reuse economic terms– RCR: Relative cost of reuse

– RCWR: Relative cost of writing for reuse

• Avoids overestimation– Avoids RCWR for non-reused components

– Adds life cycle cost savings

• Provides experience-based default parameter values• Simple Excel spreadsheet model

– Easy to modify, extend, interoperate

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COPLIMO Estimation SummaryPart I: Product Line Development Cost Estimation Summary:

# of Products 0 1 2 3 4 5

Effort (PM)

No Reuse 0 294 588 882 1176 1470

Product Line 0 444 589 735 881 1026

Product Line Savings 0 -150 -1 147 295 444

ROI 0 -1.00 -0.01 0.98 1.97 2.96

Part II: Product Line Annualized Life Cycle Cost Estimation Summary:

# of Products 0 1 2 3 4 5

AMSIZE-P 0 8.1 16.2 24.2 32.3 40.4

AMSIZE-R 0 6.1 6.1 6.1 6.1 6.1

AMSIZE-A 0 6.1 7.7 9.3 11.0 12.6

Total Equiv. KSLOC 0 20.2 29.9 39.6 49.3 59.1

Effort (AM) (*2.94) 0 59.4 88.0 116.5 145.1 173.7

5-year Life Cycle PM 0 296.9 439.8 582.6 725.4 868.3

PM(N, 5)-R (+444) 0 740.9 883.7 1026.5 1169.4 1312.2

PM(N, 5)-NR 0 590.9 1181.9 1772.8 2363.8 2954.7

Product Line Savings (PM) 0 -149.9 298.2 746.3 1194.4 1642.5

ROI 0 -1.00 1.99 4.98 7.97 10.96

Devel. ROI 0 -1.00 -0.01 0.98 1.97 2.96

3-year Life Cycle 0 -142.0 120.0 480.0

AMSIZE: Annually Maintained Software Size

Product Line Development Cost Estimation

-200

0

200

400

600

0 1 2 3 4 5 6

# of products in product line

Net

dev

elo

pm

ent

effo

rt s

avin

gs

Product Line Annualized Life Cycle Cost Estimation

-200

-100

0

100

200

300

400

500

600

700

800

0 1 2 3 4 5 6

# of products

Net

Pro

du

ct L

ine

Eff

ort

Sav

ing

s

5-year Life Cycle

3-year Life Cycle

Development

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COCOMO II• Software• Development phases• 20+ years old• 200+ calibration points• 23 Drivers• Variable granularity• 3 anchor points• Size is driven by SLOC

COSYSMO• Systems Engineering• Entire Life Cycle• 3 years old• 60 calibration points• 18 drivers• Fixed granularity• No anchor points• Size is driven by

requirements, I/F, etc

Model Differences

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COSYSMO

SizeDrivers

EffortMultipliers

Effort

Calibration

# Requirements# Interfaces# Scenarios# Algorithms

+Volatility Factor

- Application factors-8 factors

- Team factors-6 factors

- Schedule driver WBS guided by ISO/IEC 15288

COSYSMO Operational Concept

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Size Drivers

Exponential Scale Factors

SoSDefinition andIntegrationEffort

Calibration

• Interface-related eKSLOC• Number of logical

interfaces at SoS level• Number of operational

scenarios• Number of components

• Integration simplicity• Integration risk resolution• Integration stability• Component readiness• Integration capability• Integration processes

COSOSIMO

COSOSIMO Operational Concept

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Security Impact on Engineering Effort

• For software developers:– Source lines of code

increases– Effort to generate

software increases• Security functional

requirements• Security assurance

requirements

– Effort to transition also increases

• More documentation• Additional certification and

accreditation costs

• For systems engineers:– Effort to develop

system increases• Network defense

requirements • Network defense

operational concepts• Program protection

requirements• Anti-tamper

implementation

– Effort to transition also increases

• DITSCAP and red teaming

Being addressed by COSECMO Being addressed by CONIPMO

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Backup Charts

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To help people reason about the

cost and schedule implications of

their software decisions

Purpose of COCOMO II

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Major Decision SituationsHelped by COCOMO II

• Software investment decisions– When to develop, reuse, or purchase– What legacy software to modify or phase out

• Setting project budgets and schedules

• Negotiating cost/schedule/performance tradeoffs

• Making software risk management decisions

• Making software improvement decisions– Reuse, tools, process maturity, outsourcing

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Relations to MBASE*/Rational Anchor Point Milestones

App. Compos.

Inception Elaboration, Construction

LCO, LCA

IOC

Waterfall Rqts. Prod. Des.

LCA

Development

LCO

Sys Devel

IOC

Transition

SRR PDR

Construction

SAT

Trans.

*MBASE: Model-Based (System) Architecting and Software Engineering

Inception Phase

Elaboration

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Post-Architecture EMs-Product:

Very Low Low Nominal High Very High Extra High

Required Reliability (RELY)

slight inconvenience (0.82)

Low, easily recoverable losses (0.92)

Moderate, easily recoverable losses (1.00)

High financial loss (1.10)

Risk to human life (1.26)

Database Size (DATA)

DB bytes/Pgm SLOC<10

10<D/P<100 100<D/P<

1000

D/P>1000

Complexity (CPLX) _ _

See Complexity

Table_ _ _

Required Reuse (RUSE)

None Across project

Across program

Across product line

Across multiple product lines

Documentation Match to Lifecycle (DOCU)

Many lifecycle needs uncovered

Some lifecycle needs uncovered

Right-sized to lifecycle needs

Excessive for lifecycle needs

Very excessive for lifecycle needs

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Post-Architecture ComplexityControl Operations Computation al

OperationsDevice-

dependent Operations

Data Management Operations

User Interface Management Operations

Very Low … … … … …

Low … … … … …

Nominal Mostly simple nesting. Some intermodule control. Decision tables. Simple callbacks or message passing, including middleware-supported distributed processing.

Use of standard math and statistical routines. Basic matrix/vector operations.

I/O processing includes device selection, status checking and error processing.

Multi-file input and single file output. Simple structural changes, simple edits. Complex COTS-DB queries, updates.

Simple use of widget set.

High … … … … …

Very High … … … … …

Extra High

… … … … …

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Post-Architecture EMs-Platform:Very Low Low Nominal High Very High Extra High

Execution Time Constraint (TIME)

< 50% use of available execution time

70% 85% 95%

Main Storage Constraint (STOR)

< 50% use of available storage

70% 85% 95%q

Platform Volatility (PVOL)

Major change every 12 mo.; minor change every 1 mo.

Major: 6 mo.; minor: 2 wk.

Major: 2 mo.; minor: 1 wk.

Major: 2 wk.; minor: 2 days

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Post-Architecture EMs-Personnel:Very Low Low Nominal High Very High Extra High

Analyst Capability (ACAP)

15th percentile

35th percentile

55th percentile

75th percentile

90th percentile

Programmer Capability (PCAP)

15th percentile

35th percentile

55th percentile

75th percentile

90th percentile

Personnel Continuity (PCON)

48%/year 24%/year 12%/year 6%/year 3%/year

Application Experience (AEXP)

<2 months 6 months 1 year 3 years 6 years

Platform Experience (PEXP)


Language and Tool Experience (LTEX)


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Post-Architecture EMs-Project:Very Low Low Nominal High Very High Extra High

Use of Software Tools (TOOL)

Edit, code, debug

Simple, frontend, backend CASE, little integration

Basic lifecycle tools, moderately integrated

Strong, mature lifecycle tools, moderately integrated

Strong, mature, proactive lifecycle tools, well integrated with processes, methods, reuse

Multisite Development:

Collocation

(SITE)

International Multi-city and Multi-company

Multi-city or Multi-company

Same city or metro. Area

Same building or complex

Fully collocated

Multisite Development:

Communications (SITE)

Some phone, mail

Individual phone, FAX

Narrowband email

Wideband electronic communication

Wideband elect. Comm, occasional video conf.

Interactive multimedia

Required Development

Schedule (SCED)

75% of nominal

85% 100% 130% 160%

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• Initial Schedule Estimation

where estimated person months excluding Schedulemultiplier effects

• Output Ranges

- 80% confidence limits: 10% of time each below Optimistic, above Pessimistic

- Reflect sources of uncertainty in model inputs

TDEV 3.67 PM

0.280.2(B 0.91)

SCED %100

PM

Stage Optimistic Estimate Pessimistic Estimate

Application Composition 0.50 E 2.0 EEarly Design 0.67 E 1.5 E

Post-Architecture 0.80 E 1.25 E

Other Model Refinements

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Early Design vs. Post-Arch EMs:

Early Design Cost Driver Counterpart Combined Post Architecture Cost Drivers

Product Reliability and Complexity RELY, DATA, CPLX, DOCU

Required Reuse RUSE

Platform Difficulty TIME, STOR, PVOL

Personnel Capability ACAP, PCAP, PCON

Personnel Experience AEXP, PEXP, LTEX

Facilities TOOL, SITE

Schedule SCED

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Outline

• COCOMO II Overview– Motivation and Context– Model Form and Parameters– Calibration and Accuracy

• Overview of Emerging Extensions– COTS Integration (COCOTS)– Quality: Delivered Defect Density (COQUALMO)– Phase Distributions (COPSEMO)– Rapid Application Development Schedule (CORADMO)– Productivity Improvement (COPROMO)– Product Line Investment (COPLIMO)– System Engineering (COSYSMO)– System of System Integration (COSOSIMO)– Dependability ROI (iDAVE)

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USC-CSE Modeling Methodology

Analyze existing literature

Step 1

Perform Behavioral analyses

Step 2 Identify relative significance

Step 3Perform expert-judgment Delphi assessment, formulate a-priori modelStep 4 Gather project data

Step 5

Determine Bayesian A-Posteriori model

Step 6

Gather more data; refine model

Step 7

- concurrency and feedback implied

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Results of Bayesian Update: Using Prior and Sampling Information (Step 6)

1.06

Literature,behavioral analysis

A-prioriExperts’ Delphi

Noisy data analysis

A-posteriori Bayesian update

Productivity Range =Highest Rating /Lowest Rating

1.451.51

1.41

Language and Tool Experience (LTEX)

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COCOMO Model ComparisonsCOCOMO Ada COCOMO COCOMO II:

Application CompositionCOCOMO II:Early Design

COCOMO II:Post-Architecture

Size Delivered Source Instructions(DSI) or Source Lines ofCode (SLOC)

DSI or SLOC Application Points Function Points (FP) andLanguage or SLOC

FP and Language or SLOC

Reuse Equivalent SLOC = Linear(DM,CM,IM)

Equivalent SLOC = Linear(DM,CM,IM)

Implicit in Model Equivalent SLOC = nonlinear(AA, SU,UNFM,DM,CM,IM)

Equivalent SLOC = nonlinear(AA, SU,UNFM,DM,CM,IM)

Rqts. Change Requirements Volatilityrating: (RVOL)

RVOL rating Implicit in Model Change % : RQEV

Maintenance Annual Change Traffic(ACT) = %added + %modified

ACT Object Point ACT (ACT,SU,UNFM) (ACT,SU,UNFM)

Scale (b) inMMNOM=a(Size)b

Organic: 1.05 Semidetached:1.12 Embedded: 1.20

Embedded: 1.04 -1.24depending on degree of: early risk elimination solid architecture stable requirements Ada process maturity

1.0 .91-1.23 depending on thedegree of: precedentedness conformity early architecture, risk

resolution team cohesion process maturity (SEI)

.91-1.23 depending on thedegree of: precedentedness conformity early architecture, risk

resolution team cohesion process maturity (SEI)

Product Cost Drivers RELY, DATA, CPLX RELY*, DATA, CPLX*,RUSE

None RCPX*, RUSE* RELY, DATA, DOCU*,CPLX, RUSE*

Platform Cost Drivers TIME, STOR, VIRT, TURN TIME, STOR, VMVH,VMVT, TURN

None Platform difficulty: PDIF * TIME, STOR, PVOL(=VIRT)

Personnel CostDrivers

ACAP, AEXP, PCAP,VEXP, LEXP

ACAP*, AEXP, PCAP*,VEXP, LEXP*

None Personnel capability andexperience: PERS*, PREX*

ACAP*, AEXP, PCAP*,PEXP*, LTEX*, PCON*

Project Cost Drivers MODP, TOOL, SCED MODP*, TOOL*, SCED,SECU

None SCED, FCIL* TOOL*, SCED, SITE*

* Different Multipliers Different Rating Scale

RQEV

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COCOMO II Experience Factory: I

Ok?

Rescope

COCOMO 2.0Corporate parameters:tools, processes, reuse

System objectives:fcn’y, perf., quality

No

Yes

Cost,Sched,Risks

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COCOMO II Experience Factory: II

Ok?

Rescope

COCOMO 2.0Corporate parameters:tools, processes, reuse


Executeprojectto next

Milestone

Ok?

Done?

End

ReviseMilestones,

Plans,Resources

No

RevisedExpectations

M/SResults

Yes

Yes

Milestone expectations

No

Yes

Cost,Sched,Risks

No

Milestone plans,resources

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COCOMO II Experience Factory: III

Ok?

Rescope

COCOMO 2.0

RecalibrateCOCOMO 2.0

Corporate parameters:tools, processes, reuse



Milestone

Ok?

Done?

End

ReviseMilestones,

Plans,Resources

AccumulateCOCOMO 2.0

calibrationdata

No

RevisedExpectations

M/SResults

Yes

Yes


No

Yes

Cost,Sched,Risks

No


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COCOMO II Experience Factory: IV

Ok?

Rescope

COCOMO 2.0

RecalibrateCOCOMO 2.0

Corporate parameters:tools, processes, reuse



Milestone

Ok?

Done?

End

ReviseMilestones,

Plans,Resources

EvaluateCorporate

SWImprovement

Strategies

AccumulateCOCOMO 2.0

calibrationdata

No

RevisedExpectations

M/SResults

Yes

Yes


ImprovedCorporate

Parameters

No

Yes

Cost,Sched,Risks

Cost, Sched,Quality drivers

No


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New Glue Code Submodel ResultsNew Glue Code Submodel Results• Current calibration looking reasonably good

– Excluding projects with very large, very small amounts of glue code (Effort Pred):

• [0.5 - 100 KLOC]: Pred (.30) = 9/17 = 53%• [2 - 100 KLOC]: Pred (.30) = 8/13 = 62%

– For comparison, calibration results shown at ARR 2000:• [0.1 - 390 KLOC]: Pred (.30) = 4/13 = 31%

• Propose to revisit large, small, anomalous projects– A few follow-up questions on categories of code & effort

• Glue code vs. application code• Glue code effort vs. other sources

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Current Insights into Maintenance Phase IssuesCurrent Insights into Maintenance Phase Issues Priority of Activities by Effort Involved and/or CriticalityPriority of Activities by Effort Involved and/or Criticality

• Higher– training SS CC– configuration management CC– operations support CC– integration analysis SS– requirements management SS CC

• Medium– certification SS– market watch CC– distribution SS

– vendor management CC– business case evaluation SS

• Lower– administering COTS licenses CC

S - spikes aroundS - spikes around refresh cyclerefresh cycle anchor pointsanchor pointsC - continuousC - continuous

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RAD Context

• RAD a critical competitive strategy– Market window; pace of change

• Non-RAD COCOMO II overestimates RAD schedules– Need opportunity-tree cost-schedule

adjustment– Cube root model inappropriate for small

RAD projects• COCOMO II: Mo. = 3.7 ³ PM

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Work streamlining (80-20) - O

Better People and Incentives

Weekend warriors - PPOS

RAD Opportunity Tree

Eliminating Tasks

Reducing Time Per Task

Reducing Risks of Single-Point Failures

Reducing Backtracking

Activity Network Streamlining

Increasing Effective Workweek

Development process reengineering - DPRS

Reusing assets - RVHL

Applications generation - RVHL

Tools and automation - O

Work streamlining (80-20) - O

Increasing parallelism - RESL

Reducing failures - RESL

Reducing their effects - RESL

Early error elimination - RESL

Process anchor points - RESL

Improving process maturity - O

Collaboration technology - CLAB

Minimizing task dependencies - DPRS

Avoiding high fan-in, fan-out - DPRS

Reducing task variance - DPRS

Removing tasks from critical path - DPRS

24x7 development - PPOS

Nightly builds, testing - PPOS

Weekend warriors - PPOS

O: covered by

RAD

Eliminating Tasks

Reducing Time Per Task

Reducing Risks of Single-Point Failures

Reducing Backtracking

Activity Network Streamlining

Increasing Effective Workweek

Reusing assets - RVHL

Applications generation - RVHL

Schedule as Independent Variable Process

Tools and automation - O

Increasing parallelism - RESL

Reducing failures - RESL

Reducing their effects - RESL

Early error elimination - RESL

Process anchor points - RESL

Improving process maturity - O

Collaboration technology - CLAB

Minimizing task dependencies - DPRS

Avoiding high fan-in, fan-out - DPRS

Reducing task variance - DPRS

Removing tasks from critical path - DPRS

24x7 development - PPOS

Nightly builds, testing - PPOS

O: covered by

RAD Capability and experience - RCAP

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RATING

FACTOR XL VL L N H VH XH

PERS-R 10% 25% 40% 55% 70% 85% 95%

PREX-R 2mo

4 mo 6 mo 1 yr 3 yrs 6 yrs 10 yrs

I,E, C Multipliers

PM 1.20 1.13 1.06 1.0 .93 .86 .80

M 1.40 1.25 1.12 1.0 .82 .68 .56

P=PM/M .86 .90 .95 1.0 1.13 1.26 1.43

RCAP:RAD Capability of Personnel

PERS-R is the Early Design Capability rating, adjusted to reflect the performers’ capability to rapidly assimilate new concepts and material, and to rapidly adapt to change.

PREX-R is the Early Design Personnel Experience rating, adjusted to reflect the performers’ experience with RAD languages, tools, components, and COTS integration.

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RCAP Example RCAP = Nominal PM = 25, M = 5, P = 5

The square root law: 5 people for 5 months: 25 PM

RCAP = XH PM = 20, M = 2.8, P = 7.1A very good team can put on 7 people and finish in 2.8 months: 20 PM

RCAP = XL PM = 30, M = 7, P = 4.3

Trying to do RAD with an unqualified team makes them less efficient (30 PM) and gets the schedule closer to the cube root law:

(but not quite: = 9.3 months > 7 months)

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COPLIMO Inputs and Outputs

For current set of similar products,

As functions of # products, # years in life cycle

COPLIMO

Average product size, COCOMO II cost drivers

Percent mission-unique, reused-with-mods,

black-box reuse

RCR, RCWR factors,

annual change traffic

Non-product line effort

Product line investment, effort

Product line savings, ROI

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4 Size Drivers1. Number of System Requirements

2. Number of Major Interfaces

3. Number of Operational Scenarios

4. Number of Critical Algorithms

• Each weighted by complexity, volatility, and degree of reuse

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Number of System RequirementsThis driver represents the number of requirements for the system-of-interest at a specific level of design. Requirements may be functional, performance, feature, or service-oriented in nature depending on the methodology used for specification. They may also be defined by the customer or contractor. System requirements can typically be quantified by counting the number of applicable “shall’s” or “will’s” in the system or marketing specification. Do not include a requirements expansion ratio – only provide a count for the requirements of the system-of-interest as defined by the system or marketing specification.

Easy Nominal Difficult

- Well specified - Loosely specified - Poorly specified

- Traceable to source - Can be traced to source with some effort

- Hard to trace to source

- Simple to understand - Takes some effort to understand - Hard to understand

- Little requirements overlap - Some overlap - High degree of requirements overlap

- Familiar - Generally familiar - Unfamiliar

- Good understanding of what’s needed to satisfy and verify requirements

- General understanding of what’s needed to satisfy and verify requirements

- Poor understanding of what’s needed to satisfy and verify requirements

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14 Cost Drivers

1. Requirements understanding

2. Architecture complexity

3. Level of service requirements

4. Migration complexity

5. Technology Maturity

6. Documentation Match to Life Cycle Needs

7. # and Diversity of Installations/Platforms

8. # of Recursive Levels in the Design

Application Factors (8)

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Level of service (KPP) requirementsThis cost driver rates the difficulty and criticality of satisfying the ensemble of Key Performance Parameters (KPP), such as security, safety, response time, interoperability, maintainability, the “ilities”, etc.

Viewpoint Very low Low Nominal High Very High

Difficulty Simple Low difficulty, coupling

Moderately complex, coupled

Difficult, coupled KPPs

Very complex, tightly coupled

Criticality Slight inconvenience Easily recoverable losses

Some loss High financial loss

Risk to human life

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14 Cost Drivers (cont.)

1. Stakeholder team cohesion

2. Personnel/team capability

3. Personnel experience/continuity

4. Process maturity

5. Multisite coordination

6. Tool support

Team Factors (6)

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Proposed COSOSIMO Size Drivers• Subsystem size of interface software measured in effective KSLOC (eKSLOC)

• Number of components

• Number of major interfaces

• Number of operational scenarios

S1

S2

S3

S4Each weighted by

• Complexity• Volatility• Degree of COTS/reuse

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Proposed COSOSIMO Scale Factors• Integration risk resolution

Risk identification and mitigation efforts• Integration simplicity

Architecture and performance issues• Integration stability

How much change is expected• Component readiness

How much prior testing has been conducted on the components• Integration capability

People factor• Integration processes

Maturity level of processes and integration lab

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Reasoning about the Value of Dependability – iDAVE

• iDAVE: Information Dependability Attribute Value Estimator• Use iDAVE model to estimate and track software dependability ROI

– Help determine how much dependability is enough– Help analyze and select the most cost-effective combination of software dependability techniques– Use estimates as a basis for tracking performance

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iDAVE Model Framework Time-phased

information processing capabilities

Project attributes

Time-phased dependability investments

IP Capabilities (size), project attributes

Cost estimating relationships (CER’s)

Dependability investments, project attributes

Dependability attribute estimating relationships (DER’s)

Cost = f

Di = gi

Value estimating relationships (VER’s)

Vj = hj IP Capabilities dependability levels Di

Time-phased Cost Dependability

attribute levels Di Value components

Vj

Return on Investment

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Typical Value Estimating RelationshipsV

alu

e ($

)

High-Returns

Production Function Shape

Linear

Investment Diminishing Returns 1.0

Full Value

Availability

Revenue loss per hour system downtimeIntel: $275KCisco: $167KDell: $83KAmazon.com: $27KE*Trade: $8Kebay: $3K

The COCOMO II Suite of Software Cost Estimation Models

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Transcript of The COCOMO II Suite of Software Cost Estimation Models