cocomo i1 status and plans - cssecsse.usc.edu/csse/event/1997/arr/day1/2_clark... · cocomo i1...

A University of Southern California

c I S I E I Center for Software Engineering - COCOMO I1 Status and Plans

Brad Clark, Barry Boehm

USC-CSE Annual Research Review

March 10, 1997

University of Southern California c S ( E ( Center for Software Engineering 81,

Outline

COCOMO I1 Status: Brad Clark - Model calibration

- Tool Status

- Data Status

COCOMO I1 Plans: Barry Boehm I

- Tool Plans

- Model Plans

- Training and data collection plans

UNIVERSITY OF SOUTHERN CALIFORNIA

Presentation Outline: *Model Calibration

Calibration Procedures

COCOMO 11.1997 Model paramiters

Accuracy Results

USC COCOMO Software Status

COSTAR Software Status

Calibration Data Status I

BKC 2./23/97 CENTER FOR SOFTWARE ENGINEERING

UNrVERSITY OF SOUTHERN CALIFORNIA

Model Calibration Status: Three models comprise COCOMO 11:

+ Applications Composition I

+ Early Design

+ Post-Architecture

Post-Architecture model calibrated

Early Design model will be derived from Post-Architecture

Applications Composition: need data I

BKC 2/23/97 CENTER FOR SOFTWARE ENGINEERING


Calibration Process: Begin with expert-determined apriori model parameters

Collect Data

Identify and consolidated highly correlated model parameters

Statistically determine estimates of consolidated model parameters from data

Use data determined coefficients to adjust apriori model parameters

Experiment with weighting I factors



Post-Architecture Model: Non-linear model:

1 1

PM = size)^ O ~ E M , estimated

A: Multiplicative calibration variable

B : Captures relative diseconomies of scale. Consists of 5 scale factors:

B=1.01+ S F .

EM: Effort Multipliers to reflect characteristics of particular software under development.

0 Size: Derived from either Source Lines of Code or Function Points. Includes reuse and breakage effects.



Apriori Model Parameters:

I Driver / Symbol I VL I L I N I H I

FLEX SF2 0.05 0.04 0.03 0.02 I RESL SF3 0.05 0.04 0.03 0.02 I TEAM I SF4 1 0.05 10.04 1 0.03 10.02 1 I PMAT I SF5 1 0.05 10.04 / 0.03 )0.02 ( 1 RELY I EM, 10.75 10.88 11.00 11.15 1 I DATA 1 EM2 I I CPLX I EM, 10.7510.88/ 1.001 1.151 IRUSEI EM, I ~ ~ 0 . 8 9 ~ 1 . 0 0 ~ 1 . 1 6 ~ I DOCU I EM, ( 0.85 1 0.93 / 1 .OO 1 1 .08 1 I TIME I EM, I I

( PVOL I EM, I BKC 2/23/97 CENTER FOR SOFTWARE ENGINEERING

UNIVERSITY OF SOUTHERN CALIFORNU

I Driver I Symbol I VL I L N H I V H I X H I

BKC 123197

ACAP 1 EM9 1 1.5 1 1.22 1 1 .OO 1 0.83 / 0.67 1 I PCAP I EMlo ~ 1 . 3 7 ~ 1 . 1 6 ~ 1 . 0 0 ~ 0 . 8 7 / 0 . 7 4 ~ I I PCON I EM,, 11.261 1.11 11.0010.91 10.831 I

I SITE I EM,, 11.24 11.10 11.00 10.92 / 0.85 10.79 1

AEXP PEXP LTEX

CENTER FOR SOFTWARE ENGINEERING

EMlP EM,, EMl4

SCED

1.23 1.26 1.24

EM,,

1 . 1 1.12 1 1

1.23

1.00 1.00 1.00

1.08

0.88 0.88 0.9

1.00

0.80 0.80 0.82

1.04 1.10


Data Collection: Define the data needed (to completely describe the Post Architecture Model) .

Collect data with a paper form or a computer software tool

Affiliate Organizations providing majority of data.

+ Historical - whole project

Site visits or phone interviews to record data

Enter in data into the re~ositorv

Data is labeled with generic id

Stored in locked room

Limited access by researchers

Data Consistency checking and conditioning BKC 2/23/97 CENTER FOR SOFTWARE ENGINEERING


Consolidated Highly Correlated Parameters :

1 TlME 1 STOR IACAP I PCAP TIME 11.0000 1

ACAP 1-0.2855 1-0.0769 I 1.0000 I

Combined (for calibration purposes only):

+ TIME & STOR into RCON (Resource Constraints)

PCAP

+ ACAP & PCAP into PERS (Personnel Factors)

-0.0027 -0.20 15

Thus, calibrated 15 effort multipliers instead of 17

BKC 2/23/97

0.7339


1.0000


Expanded Post-Architecture Model: Distribute the Scale Factors

2 1 predictor variables: 15 Effort Multiplier Coefficients + 5 Scale Factor Coefficients + overall A constant:

,S t = A ( S i z e ) E M ,-• E M 1 5

1.01

Log Transformed Model: Regression analysis will derive the coefficients, A and bi, for each factor

1

In(PM est )- ln(Si~e)~l .Ol= A+b,SF,ln(Size)+~~~+b,,ln(EMl,)

BKC U23197 CENTER FOR SOFTWARE ENGINEERING


Example of Applying Coefficients to Model Apriori Parameters:

I

Driver I Symbol PREC I SF, FLEX I SF, RESL 1 SF, TEAM I SF, PMATI SF, RELY I EM, DATA I EM, CPLX I EM,

DOCUI EM,

PVOL I EM, PERS I EM,,, PCON 1 EM,, AEXP I EM,,

CENTER FOR SOFTWARE ENGINEERING BKC 2/23/97

UNIVERSITY OF SOUTHERNCALIFORNIA

RUSE Effort Multiplier: Example of the effect of a negative coefficient


+ 10% Regression -a- Regression Results


Distribution of RUSE:

Frequency 30

BKC 2/23/97

0.9 1 .O 1 .I 1.2 I .3 I .4 1.5 1.6

RUSE



Evolving Model Values:

100% Data Driven

BKC 2/23/97

100% Expert Driven

Number of projects used in calibration


UNNERSITY OF SOUTHERN CALIFORNIA

Aposteriori Model Parameters Using 10% of data-determined and 90% of apriori

Effort constant, A: 2.45

FLEX I SF, 10.0607 10.0486 IRESL 1 SF, 10.0422 10.0338 TEAM 1 SF, 10.0494 (0.0395 IPMAT I SF, 10.0454 10.0364 RELY I EM, 10.75 10.88

~CPLX I EM, 10.75 10.88

~DOCU I EM, 10.89 10.95 ITlME I EM, I I ISTOR I EM, I I



Calibrated schedule constant, A: 2.66 (apriori value was 3.0)



Presentation Outline: Model Calibration

Calibration Procedures I

COCOMO I1 1997 Model Parameters

Accuracy Results

USC COCOMO Software Status

COSTAR Software Status

Calibration Data Status



Accuracy Results: Forecast accuracy measured with Proportional Error (PE) :

[pn/r,y, + p ~ ~ c t ] - 1, (PMe,yt - PMact) 2 0 - [PM,, + PM,, ] + 1, ( PMest - PMact ) < 0

I Effort I Before Stratification ( After Stratification

I Schedule ( Before Stratification I After Stratification



Effort Proportional Error before Stratification



Effort Proportional Error after Stratification

BKC 2J23197

Organization Number



USC COCOMO Software Status: There is a initial version available for MS Windows, Sun OS, and Java

+ Has new calibrated values

+ Confidence ranges (optimistic, most likely, pessimistic)

+ User definable Cost Drivers: USR1, USR2

+ Schedule input is now project wide

+ New reference manual

+ New values can be manually input for all cost drivers

+ Version changed to COCOMO II.199Y .X (where Y is the year number and X is the version within that



USC COCOMO Future Work:

BKC 2l23197 CENTER FOR SOFTWARE ENGINEERING

Entry of actuals for periodic tracking of project and data submission

Calibration of constant and exponent

Incremental ratings between Very Low, Low, Nominal, High, Very High, Extra High

Text entry for SU, AA, UNFM

New Help file


COSTAR Software Status Commercialized version of COCOMO

Beta version of COCOMO I1 model available

New values will be put in the model soon



Calibration Data Status More project data is required to facilitate better calibration of the general COCOMO I1 Post-Architecture model

We hope the use of USC COCOMO software will facilitate collection and submission of data

If you calibrate the model to your local organization (constant and exponent) - we would like to have your observations in our repository to be used for full model calibration

We plan to make annual updates to the cost driver values and release them on a regular cycle.

BKC 2/23/97 CENTER FOR SOF.TWARE ENGINEERING


Information Sources : Phone: 213-740-6470

Email : [email protected]

Web site: http://sunset.usc.edu/COCOMOII/Co~~mo.html

+ Affiliate Prospectus

+ Model Definition Manual (ver. 1.4)

+ Data Collection Form (ver. 1.6)

+ USC COCOMO Software and User's Manual

+ Java COCOMO I

+ Little Expert COCOMO Calculator

BKC a23197 CENTER FOR SOFTWARE ENGINEERING

University of Southern California I C I S I E I Center for Software Engineering L

Outline

COCOMO I1 Status: Brad Clark - Model calibration

- Tool Status - Data Status

-F COCOMO I1 Plans: Barry Boehm - Tool Plans

- Model Plans - Training and data collection plans

Universi~y of Southern California

( c ( S I E ( Center for Software Engineering - Tool Plans: USC COCOMO 11.1997.1

Calibration to an organization's data - Effort andlor schedule

- Coefficient or also exponent

Intermediate rating levels

Updated Madachy risk assessment model 1

Added reuse parameters: SU, AA, UNFM

Calibration.

- Provide a way of capturing and retaining a set of projects.

- Capability of changing C (constant) and E (exponent) from cocomo equation.

- Provide users with 2 ways of using C and E. I

- Standard cocomo values

- Calibrated values

University of Southern California

C I S B E Center for Software Engineering - Model Plans: Affiliate Priorities

14 Activity distribution

13 COTS integration costs

l2 Sizing improvements

5 C~st/schedule/quality tradeoffs

5 Life cycle tradeoff models

A University of Southern California I C I S I E I Center for Software Engineering -

Effort Distribution bv Activitv EffortIFP varies by language level (LL) - But so does effortISLOC! I

Proposed approach - SLOC, LL ==> Effort:

Determine equivalent 3GL SLOC (3 SLOC) via backfiring

Compute effort as F (3 SLOC)

Apply LL stage multipliers to obtain activity distribution, total effort I

- UFP, LL ==> Effort Determine 3 SLOC by backfiring

Compute effort a F(3 SLOC)

Apply LL stage multipliers to obtain activity distribution, total effort

University of Southern California Center for Software Engineering

4GL Cost and Schedule Effects

1

+ Correspondence school information system + Estimated size: 15 KDSl ALL [4GL], 95 KDSl

COBOL + Actual size: 13.9 KDSl ALL, 93.6 KDSl equiv.

COBOL + Data on phase distribution of effort and

schedule I


4GL Estimates vs. Actuals

Quantity

Size Schedule Effort Plans&Rqts Prel Design DD/CUT/J&T

COCOMO- COCOMO- COBOL 4GL

Recom. Actual Use

13.9 12.0 4GLA1.6 61.6 Mix:62.7 14.9 COBOL 20.5 3*4GL

26.2 4GL


Effort Distribution Relative to 3GL Development

Rapid APP* Devel.

LCO, LCA SAT

Spiral-type Ev. Dev., Spiral

I

LLICa LCA 1

SAT

Waterfall, W'fall, IncDev, EvDev,

SYS Spiral-type Devel

I

Spiral-type

I

Spiral, Design-to-Cost, etc.

Det. Design Code, Integ., Test

University of Southern California c I S I E ~ Center for Software Engineering

Proposed UML-Based Sizing Model

Rational' s Universal Modeling Language (B ooch, Jacobson, 'Rumbaugh) approaching de-facto OOD standard

A UML-based early sizing metric would address two major current short falls with Funciton Points - Automated counting

I

- Object-orientation

Rational (Walker Royce) is interested in pursuing such a project

Would need some Affiliates to provide data

Model Elements Class

0 A set of objects that share a common structure and a common behavior

Use caselcollaboration 0 A named behavior involving the collaboration of a society of

objects

Stateloperation 0 The condition of an object; an activity

Interface 0 The public part of an object

Thread

n An active class, capable of concurrent activity with other active classes

Model Elements (cont) Component

a A reusable part, typically having both logicalas well as physical aspects

Node .. .

I 3 A hardware device upon which software may reside andlor execute

Package A container of elements

Note , . A comment, explanation, or annotation

Diagrams Class diagram State machine diagram Sequence diagram Collaboration diagram Activity diagram Use case diagram Component diagram Deployment diagram

Architecture

Enduser Programmers Functionality Softwarn management

I - Logical View Component

AnalysMesters Use Case Behavior

Concurrency I I Dsployment I " i i v i w I System Integrators Performance Scalability Throughput

System Engineering srstem *polow

Delivery, installation Communication

A University of Southern California

c [ S I E I Center for Software Engineering - Training and Data Collection Plans

USC COCOMO 11.199'7.1 or COSTARICALICO as data colleciton instruments

Proposed role for Don Reifer - Meet broader need for COCOMO I1 training

- Make data collection easier, *more efficient

ROLE FOR DON REIFER

Assist team in calibrating COCOMO-I1 - Use his databases, when applicable, to help

calibrate the model

- Run statistical tools to generate goodness of fit and other meaninghl measures

Provide ideas based on his extensive cost modeling experience

Develop public training and help get data for the model's continued refinement

WIN-WIN SITUATION USC and Affiliates get: - Calibration data and the results of an analysis

of about 500 projects

- Extensive knowledge base of experience of one of the leaders in field of parametric modeling

- Public courseware when they need it

Reifer Consultants (RCI) gets: - In-depth knowledge of COCOMO-I1

- Ability to consult and market COCOMO-I1 courseware and make a profit

USCIRCI AGREEMENTS

RCI will not market COCOMO packages - Their focus will be training and consulting

RCI will not market competing packages - Their agreement with Resource

has been terminated

- They have elected to discontinue their SoftCost product line in the

Calculations

support for future

USC will cooperate and get data and .

feedback from the trained model users

cocomo i1 status and plans - cssecsse.usc.edu/csse/event/1997/arr/day1/2_clark... · cocomo i1...

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