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www.davidconsultinggroup.com

PROJECT SIZING AND ESTIMATING -EFFECTIVELY USING

FUNCTIONAL MEASUREMENT

Southern California Software Process Improvement Network

Copyright © 2005. The David Consulting Group, Inc. 2

Topics

• The User Perspective

• Program Start-up

• Characteristics of an Effective Sizing Metric

• Use of Function Points

• Project Estimation

• Quantitative & Qualitative Assessments

• Establishing and Using Baseline Data

• Modeling Improved Performance


“How do we indicate the value of sizing to our users/customers? When we say that a project represents 50 Function Points from the 'users perspective‘, what does this really mean to the user? or to the developer? How do we engage both parties in understanding size?

The User Perspective

• Functional ‘value’ delivered to the user/customer• Comparative analysis (size, cost per unit of work, defects)

• Functional description and accountability (user)

• Management of delivery expectations

• Credibility in project estimation


Program Start-up

• Planning• How will the information be used (objectives)?• Who is counting?• What is being counted?• History versus industry?

• Path of least resistance• Easy to count (transaction based)• Agreeable user

• Culture• Internal versus external• Pilot/rollout versus organization-wide

• Scope (what doesn’t get counted)• Continually evaluating the program


Costs/Required Resources,Start-up and Ongoing

• Less than 1% of total budget (labor)• Internal

• Training (several days)• Mentoring• Frequency/ skill level• User group knowledge sharing

• External• Economies of scale• Awareness & orientation• Internal resources required

• Positioned for Success• Other measures to be utilized • Process improvement activities monitored and measured


1

DESIGN TEST IMPLEMENT DEFINE BUILD

SIZING

When to Size

1) Initial sizing during or after Requirements Phase2) Subsequent sizing after System Design or when Change occurs3) Final sizing after Install

SIZINGSIZING

2 3


• Meaningful to developer and user/customer

• Defined (industry recognized)

• Consistent (methodology)

• Easy to learn and apply

• Accurate, statistically based

• Available when needed (early)

• Addresses project level information needs

Characteristics of an Effective Sizing Metric


Function Point Analysis is a standardized method for measuring the functionality delivered to an end user.

Function Points - An Effective Sizing Metric

Benefits:• Quantitative (Objective) Measure• Industry Data as Basis for Comparison • Expectations (Perceived Customer Value) Managed • Software Process Improvement Requirements Satisfied


• A vehicle to estimate cost and resources required for software development, enhancements and/or maintenance

• A tool to quantify performance levels and to monitor progress made from software process improvement initiatives

• A tool to determine the benefit of an application to an organization by counting functions that specifically match requirements

• A tool to size or evaluate purchased application packages

Benefits of Using Function Points


Approach to Function Points

• A function point count is performed to produce a functional size measure

• The size can be used to generate project estimates• Estimates should be based upon delivery rates

• Analysis - plan versus actual comparisons • How good is the information received during

requirements? • How good (accurate) is project estimating?


• Review the available documentation

• Meet with SME to gain a thorough understanding of the functionality

• Apply the function point methodology, and compute a functional size

• Generate an estimate based on available delivery rates

Function Point Counting Process


• Inputs• Outputs• Inquiries• Internal Logical Files• External Interface Files

Input Inquiry

Output

InternalLogical

Files

External Interface

File

Five key components are identified based on logical user view

Application

The Function Point Methodology


USER

LIST OF MOLDSWORK CENTERS

PARTS

PLANT MOLDS

PLANT INFORMATION CENTER

USER

BILL OF MATERIALS

PARTS LISTING

USER

ORDERPARTS

USER

CHANGEBILL

Inquiries

Internal Logical Files

Output

Inputs

InterfaceVENDORSUPPLY

VENDOR INFORMATION

Logical View of User Requirements


Complexity

RecordElement

Types

Data Elements (# of unique data fields)

or File Types Referenced

Low Average High Low

Low Average

HighAverage High

Components: Low Avg. High Total

Internal Logical File (ILF) __ x 7 __ x 10 __ x 15 ___

External Interface File (EIF) __ x 5 __ x 7 __ x 10 ___

External Input (EI) __ x 3 __ x 4 __ x 6 ___

External Output (EO) __ x 4 __ x 5 __ x 7 ___

External Inquiry (EQ) __ x 3 __ x 4 __ x 6 ___

___Total Unadjusted FPs

Data Relationships

Each identified component is assigned a Function Point size value based upon the make-up and complexity of the data

1 3

3



General System Characteristics

Data Communication On-Line Update

Distributed Data Processing Complex Processing

Performance Objectives Reusability

Heavily Used Configuration Conversion & Install Ease

Transaction Rate Operational Ease

On-Line Data Entry Multiple-Site Use

End-User Efficiency Facilitate Change

The final calculation is based upon the Unadjusted FP count X VAF

14 Optional General Systems Characteristics are evaluated and used to compute a Value Adjustment Factor (VAF)



Function Point Calculation

Enhancement FPs as they relate to existing master count:• External Inputs (EI) (2) – Add/Change Account; change; high

complexity; total unadjusted FPs = 2 x 6 = 12• External Input (EI) –Issue Material; change; high complexity; total

unadjusted FPs = 1 x 6 = 6• External Input (EI) – Add Tax; change; low complexity; total unadjusted

FPs = 1 x 3 = 3

Total Unadjusted FPs: 21

Value Adjusted Factor: 1.01

Total Adjusted FPs: 21


Example – Counting Accuracy

Estimate:• Resulting size = 150 function points• Matching profile, rate of delivery = 10 FP/EM• Estimated effort = 15 effort months

Actuals: Results

Size 175 fps +17%

Effort 19 effort months +27%


Example – Scope Accuracy

Counting Activity FP• Resulting Requirements size = 120• Resulting Design size = 144• Resulting Install size = 174

Analysis:Inputs Outputs Inquiries Interfaces Files Total 75 10 15 7 13 120 80 25 17 7 15 144 95 40 17 7 15 174


PROJECTSIZE

X X RISKFACTORS

PROJECTCOMPLEXITY

DEFINITION CAPABILITY ESTIMATE

Sc

he

du

le

Effort Costs

REQUIREMENT

FUNCTION POINT ANALYSIS

Project Estimation


Capability Analysis

• Collect project data• Project metrics (e.g., effort, size, cost, duration, defects) • Project characteristics• Project attributes (e.g., skill levels, tools, process, etc.) • Project complexity variables

• Analyze data• Performance comparisons (identification of process

strengths and weaknesses)• Industry averages and best practices • Performance modeling (identify high impact areas)


DCG Data Base

Characteristics Complexity Variables

Project TypePlatformData BaseMethodLanguage

Metrics Attributes

Size Management Cost Definition Effort DesignDuration BuildDefects Test

Environment

Logical Algorithms Code StructureMathematical Algorithms PerformanceData Relationships MemoryFunctional Size SecurityReuse Warranty

ProcessSkill LevelsQuality PracticesMeasures


Quantitative & Qualitative Assessments

MEASURESResearch

PROFILESPERFORMANCE LEVELS

CHARACTERISTICS

Software SizeLevel of EffortTime to Market

Delivered DefectsCost

Skill LevelsAutomation

ProcessManagement

User InvolvementEnvironment

Analysis

Results • Correlate Performance Levels to Characteristics• Substantiate Impact of Characteristics• Identify Best Practices


PROJECT SIZE andCOMPLEXITY

RATE OFDELIVERY

DEFINITION CAPABILITY ESTIMATE

Sc

he

du

le

Effort Costs

REQUIREMENT

FUNCTION POINT SIZE

Estimating Using Historical Delivery Rates

FUNCTION POINTS Per EFFORT MONTH


Analysis of Results

• Analyze estimating accuracy• Plan vs. actual comparisons• Effectiveness of delivery rates

• Evaluate the system level documentation • Change in scope (size) through the various stages• Clarity of requirements and design documents

• Recommend improvements• Improve the level of documentation for more accurate

sizing• Establish a more effective estimating practice


D

ProductDeliverable

Performance Profiles

Time to Deliver - Duration - Number of daysLevel of EffortDefects

SizePlatformLanguage

SIZEPROJECT

MEASURES PROFILES

Rate of DeliveryTime to MarketDefect Density

A

BC

D

A

BC

D

:

136

276

435558759

10 mnths

35 effort mnths

10 defects

Develop a Baseline of Data


Establish A Baseline

SoftwareSize

Size isexpressedin terms of functionalitydelivered to theuser

Rate of delivery is a measure of productivity

Rate of DeliveryFunction Points per Person Month

0200

400600800

100012001400

1600

180020002200

0 2 4 6 8 10 12 14 16 18 20 22 24 26 28 30 32 34 36

Performance Productivity

A representative selectionof projects is measured

Organizational Baseline


Compare To Industry Benchmarks

Industry baseline performance

Rate of DeliveryFunction Points per Person Month

0200

400600800

100012001400

1600

180020002200

0 2 4 6 8 10 12 14 16 18 20 22 24 26 28 30 32 34 36

SoftwareSize


Function Points Per Person Month

Web 22e-business Web 12

Client Server 15Main Frame 13

Vendor Packages 19Data Warehouse 11

Average of Recent Projects Across Different Platforms


Function Points Supported By One FTE

Web 748e-business Web 464Vendor Packages 760Data Warehouse 546

Client Server 642Main Frame 943AS 400 597

Average of Support Provided for Corrective Maintenance by One FTE


Analyze Results

COLLECTQUANTITATIVE DATA

COLLECTQUALITATIVE DATA

ProcessMethods

SkillsTools

Management

MeasuredPerformance

CapabilityProfiles

BaselinePerformance

Collection

Analysis

Results

Action OpportunitiesFor Improvement

Best Practices

SizeEffort

DurationCost

Quality


• Develop parametric models that utilize historical data points for purposes of analyzing the impact of

selected process improvements

• Provide a knowledge base for improved decision making

• Identify areas of high impact (e.g., productivity and quality

• Create an atmosphere of measuring performance

• Opportunity for comparison to industry best practices

Model Performance


COLLECTQUANTITATIVE DATA

SizeEffort

DurationCost

Quality

MeasuredPerformance

Average Project Size 133Average FP/SM 10.7Average Time-To-Market (Months) 6.9Average Cost/FP $939Delivered Defects/FP 0.0301

Baseline Productivity

Quantitative Assessment• Perform functional sizing on all selected projects.• Collect data on project level of effort, cost, duration

and quality. • Calculate productivity rates for each project, including

functional size delivered per staff month, cost per

functional size, time to market, and defects delivered.

Results

Quantitative Performance Evaluation


COLLECTQUALITATIVE DATA

ProcessMethods

SkillsTools

Management

CapabilityProfiles

Accounts Payable 55.3 47.73 82.05 50.00 46.15 43.75 50.00Priotity One 27.6 50.00 48.72 11.36 38.46 0.00 42.31HR Enhancements 32.3 29.55 48.72 0.00 42.31 37.50 42.31Client Accounts 29.5 31.82 43.59 0.00 30.77 37.50 42.31ABC Release 44.1 31.82 53.85 34.09 38.46 53.13 42.31Screen Redesign 17.0 22.73 43.59 0.00 15.38 0.00 30.77Customer Web 40.2 45.45 23.08 38.64 53.85 50.00 34.62Whole Life 29.2 56.82 28.21 22.73 26.92 18.75 53.85Regional - East 22.7 36.36 43.59 0.00 30.77 9.38 30.77Regional - West 17.6 43.18 23.08 0.00 26.92 9.38 26.92Cashflow 40.6 56.82 71.79 0.00 38.46 43.75 38.46Credit Automation 23.5 29.55 48.72 0.00 38.46 6.25 26.92NISE 49.0 38.64 56.41 52.27 30.77 53.13 53.85Help Desk Automation 49.3 54.55 74.36 20.45 53.85 50.00 38.46Formula One Upgrade 22.8 31.82 38.46 0.00 11.54 25.00 46.15

Design Build Test EnvironmentProject Name Profile Score Management Definition

Results

Qualitative Assessment• Conduct Interviews with members of each project team. • Collect Project Profile information. • Develop Performance Profiles to display strengths and

weaknesses among the selected projects.

Qualitative Performance Evaluation





Baseline Productivity


Productivity Improvement

Process Improvements:• Code Reviews and Inspections• Requirements Management• Defect Tracking Configuration Management

Performance Improvements: Productivity ~ +131% Time to Market ~ -49% Defect Ratio ~ -75%

SAMPLE

DATA



Modeled Improvements


Conclusions

• Project Management can be successful

• Requirements can be managed

• Projects can be sized

• Performance can be successfully estimated

• Process improvement can be modeled

• Measurement can be accomplished


Contact Information

David Consulting Group web site:www.davidconsultinggroup.com

David Garmus [email protected]

http://www.davidconsultinggroup.com/

mailto:[email protected]


Contact Information

International Function Point Users Group (IFPUG)www.ifpug.org

Practical Software and Systems Measurement (PSM)www.psmsc.com

Software Engineering Institute (SEI)www.sei.cmu.edu

Software Quality Engineering (SQE)www.sqe.com

Quality Assurance Institute (QAI) www.qaiusa.com

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