presentation

University of Southern California

Center for Systems and Software Engineering

A Tractable Approach to Handling Software Productivity Domains

Thomas TanBrad Clark

24th International Forum on COCOMO and Systems/Software Cost Modeling

November 3, 2009

24th International Forum on COCOMO and Systems/Software Cost Modeling 2



Table of Contents• Overview• Operational Context• Software Application Difficulties• Productivity Group Matrix

– Example Mapping of Systems– Aerospace Project Data Analysis

• COCOMO II Implications• Next Steps• Q&A

This work is sponsored by the Air Force Cost Analysis Agency




Overview• Conventional labels used in

Software Productivity Domains use system engineering terms

• Systems can be comprised of many different software applications, e.g. Command and Control

• Productivities are influenced by the difficulty of the application type and the operating environment

• Many applications in a system can exhibit different productivities

Command & Control System

Database

Controls & Displays

Simulator

Training System

Telemetry Processing

Sensor Processing

Communications

Information Assurance

Testbed




Motivation• Our motivation is to create a new productivity classification

system that can be– Understood by both cost estimators and engineers– Flexible enough to accommodate a wide range of software

applications

• This presentation discusses this new approach• As a work-in-progress, we welcome comments and suggestions• Please join us at the workshop to discuss this approach and its

usage




Overview• This approach employs general descriptions to characterize the

intended “operational context” (environment) and an application’s “degree of difficulty”:– Operational Context describes the intended environment, platform or

target host on which the software application will operate – The Application Difficulty is the degree of difficulty in specifying,

developing, and testing a software application




Operational Context

Very Unconstrained Virtually unlimited

resources Accessible physical

environment Stationary

conditions Stable platform

Very Constrained Limited physical

volume, power & weight

Unmanned Inaccessible

physical environment

Evolving platform Hard real-time

Un-Manned Space Bus

Manned Space Vehicle

Missile

Un-Manned Airborne

Airborne Shipboard

Mobile Ground

Land Site

Har

dwar

e P

latf

orm

s

Dimensions of constraints include electrical power, computing capacity, storage capacity, repair capability, platform volatility, physical environment accessibility, etc.




Operational Context• Very Unconstrained

– Unlimited electrical power with backup battery– Regular upgrades and maintenance– Heavily COTS-hardware and COTS-software dependent– Supports multiple users– Limited loss during system down time

• Unconstrained– Operating environment may move over land and water– Electrical power is limited by mobility (size dependent)– Computing and storage capability depends on ability to carry weight and size– Upgrades and maintenance are not frequent– Components are modular and cannot be disassembled




Operational Context• Constrained

– Similar to unconstrained in terms of upgrade, maintenance, and component structure

– Operating environment may move through the air– Electrical power, computing and storage capability are limited due to weight

constraints

• Very Constrained– Battery is the primary source of electrical power. – Components are miniaturized and custom-made with special tools to access– No ability to upgrade and maintain system after deployment

• Highly Constrained– Hardware is required to survive in adverse conditions– Limited software control– Real-time sensor inputs and commands– Upgrades and maintenance is not feasible after deployment– Unique system platform that is evolving in maturity as system is being

developed




Software Application Difficulties

Very Easy

Simple handling of events / inputs

Relies on O/S or middleware for control

Can be restarted with minor inconvenience

Very Challenging Autonomous

operation Extremely high

reliability Complex algorithms Micro-second

control loop Micro-code

programming

Firm

ware

(R

OM

)

Att

itude C

ontr

ol

Guid

ance /

Navig

ation

Radar

/ S

onar

/ T

ele

metr

y

pro

cessi

ng

Pro

cess

Contr

ol

Seis

mic

Pro

cess

ing

Fact

ory

Auto

mation

Dig

ital S

witc

hes

& P

BX

Case

Tools

/ C

om

pile

rs

Website

Auto

mation

Busin

ess

Syste

ms

Difficulty would be described in terms of required software reliability, database size, product complexity, integration complexity, information assurance, real-time requirements, different levels of developmental risks, etc.




Software Application Difficulties• Very Easy

– Risks are well understood with little loss from failure– Business or operational logic is straightforward– Limited interface to other software applications– Mostly stand-alone functionality– Simple tests

• Easy– Not a new type of application– Risks are understood and mitigation strategies exists– Business or operational logic is straightforward– Requires low reliability due to small or little loss when unavailable– Limited external interface and security requirements

• Nominal– Somewhat complicated business logic– Risks exists and may need additional study to find mitigation– May require distributed environment with additional security requirements– Moderate, easily recoverable loss for nominal reliability– Not a new type of application




Software Application Difficulties• Challenging

– High reliability due to greater impact of loss or high probability of risk– Risks are challenging to resolve– Very complicated business logic, external storage may be necessary due to distributed

environment– New type of application– Hard real-time control and security requirements– Additional communication interfaces necessary for external components or systems

• Very Challenging– Extremely complicated business logic– Risks are very challenging to resolve and loss is great (disastrous consequences)– Many automated controls with limited human control– New type of application– Hard real-time control and security requirements– Communication to external components through different interfaces




Productivity Group Matrix

Productivity Group Matrix

5,55,45,35,25,1Very

Challenging

4,54,44,34,24,1Challenging

3,53,43,33,23,1Nominal

2,52,42,32,22,1Easy

1,51,41,31,21,1Very Easy

Extremely Constrained

Very ConstrainedConstrained

Uncon-strained

Very Uncon-strained

Application Difficulty

Operational Context

• The intersection of an Operational Context rating and Application Difficulty rating represent a Productivity Group

– Each group includes the number of project data points, data range, simple cost estimating relationship (of the form: Y = aXb), standard error, percent bias, and the coefficient of determination, R2




Example Mapping to Systems• Very Unconstrained and Very Easy may include systems like:

– Business Analysis Systems– Data Warehousing Systems– Diagnostic Systems

• Very Unconstrained and Easy may include systems like:– Logistics Systems– Training Systems– Support Systems– Test Systems

• Constrained and Challenging may include systems like:– Flight Systems– Radar Systems– Signal Processing

• We welcome comments and suggestions on mappings of systems to the productivity groups.




Data Analysis Experiment• Extracted data from Aerospace Corp. report*

– 188 data records– 4 Operating Environments– 8 Application Domains

• Our experiment– Select a Challenging Application: Signal Processing– Select an Easy Application: Test– Calculate estimating relationship (in Y = aXb form), standard error, and R2

– Our hypothesis is that Easy Applications will have higher productivity than Challenging Applications

– Operational Context not considered in this experiment

* Gayek, Long, Bell and Larson, “Software Cost and Productivity Model,” Aerospace Report ATR-2004(8311)-1, The Aerospace Corporation, El Segundo, CA, Feb 04




Aerospace Project Data Analysis

• Signal Processing– 40 records– Estimating relationship for

effort• Effort = 11.82 x (Size) 0.93

• R2 = 0.7• Standard Error = 0.57

Size vs. Effort

0.0

200.0

400.0

600.0

800.0

1000.0

1200.0

1400.0

0.0 50.0 100.0 150.0 200.0

Size (KESLOC)E

ffor

t (P

M)




Aerospace Project Data Analysis

• Test:– 16 records– Estimating relationship for

effort• Effort = 7.46 x (Size) 0.97

• R2 = 0.68• Standard Error = 0.53

Size vs. Effort

0.0

100.0

200.0

300.0

400.0

500.0

600.0

0.0 10.0 20.0 30.0 40.0 50.0

Size (KESLOC)E

ffo

rt (

PM

)




Aerospace Project Data Analysis• The estimating relationships were used to compare Easy and

Challenging Application Difficulty– Size in KSLOC– Effort in PM– Productivity in SLOC/PM

13138251623096500

12316311571273200

117856154650100

11144915133250

10419214713620

991011447010

ProductivityEffort ProductivityEffortSize

Challenging: Signal ProcessingVery Easy: Test




Challenging Difficulty Plot

Challenging Application Difficulty

0

200

400

600

800

1,000

1,200

1,400

0 20 40 60 80 100 120 140 160

Size (ESLOC)

Eff

ort

(P

M)

Very Unconstrained Unconstrained Constrained

Very Constrained (Power (Very Constrained (Power (Unconstrained

(Power (Very Unconstrained

Very Unconstrained

UnconstrainedVery Constrained




Easy Difficulty Plot

Easy Application Difficulty

0

100

200

300

400

500

600

0 5 10 15 20 25 30 35 40 45 50

Size (KESLOC)

Eff

ort

(P

M)

Very Unconstrained Constrained Very Constrained

Unconstrained (Power (Very Unconstrained

Very Unconstrained




Productivity Group Matrix – 2nd Use• Productivity Group Matrix is “Cost Model” independent• The matrix could be used to “pre-set” parameters for different models, e.g.

– COCOMO II– SEER– True-S– SLIM

• Possibly, the matrix could be extended to have different distributions for effort and duration for lifecycle activities, i.e.

– Each cell has its own distribution

5,55,45,35,25,1Very

Challenging

4,54,44,34,24,1Challenging

3,53,43,33,23,1Nominal

2,52,42,32,22,1Easy

1,51,41,31,21,1Very Easy


Very ConstrainedConstrained

Uncon-strained

Very Uncon-strained

Application Difficulty

Operational Context




COCOMO II Implications

Productivity Groups may be used to to pre-set COCOMO II Parameters. This is also a work-in-progress.

XHXHVHHNSTOR

XHXHVHHNTIME

VHVHHNLPVOL

XHVHHN, LVLCPLX-Device

VLVLLNHRESL

VLVLLNHPRED


Very ConstrainedConstrainedUnconstrained

Very Unconstrained

COCOMO II Parameters

Pre-sets driven by Operational Context*

* These ratings have not been verified with data, they for illustration only




COCOMO II Implications

Pre-sets driven by Application Difficulty*

VHVHHNLDATA

XHVHHN, LVLCPLX-UI

XHVHHN, LVLCPLX-Data

XHVHHN, LVLCPLX-Computations

XHVHHN, LVLCPLX-Control

VHHNLVLRELY

VLVLLNHRESL

VLVLLNHPRED

Very ChallengingChallengingNominalEasyVery EasyFactors

* These ratings have not been verified with data, they for illustration only




COCOMO II Implications• Factors captured by local calibration of COCOMO constants A

and B:– Security and Information assurance (given complexity is at least high)

• Very Easy would have no security or information assurance requirements

• Very Challenging would require the software to authenticate access, handle denial of service attacks, ensure data integrity, etc.

– Components or Sub-system Interactions• Heterogeneous components require special interfaces to

communicate with each other• Homogeneous components are generally built with similar

communication interface




COCOMO II Implications• COCOMO Parameters not addressed:

– Development Flexibility– Team Cohesion– Process Maturity– Developed for Reuse– Analyst Capability– Programmer Capability– Personnel Continuity– Applications Experience– Platform Experience– Language and Tool Experience– Use of Software Tools– Multi-Site Development

• These are usually driven by business decisions

• May be indirectly driven by Difficulty or Context




Next Steps• The proposed approach to classifying software application

productivities by Operational Context and Application Difficulties needs to be verified with the government agencies and industrial communities that will use the approach for software project cost estimates.

• Solicit input through workshops and document reviews– Verify ratings for operational context and application difficulties.– Assistance in characterization of applications and determining where

they fit in the productivity group matrix

• Analyze project cost data for productivity: each project will be characterized by Operational Context and Application Difficulties

• If you would like to participate — please give us your contact information




Questions?

For more information, contact:Thomas [email protected]

mailto:[email protected]

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