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Software Cost Estimation and COCOMO II
❚ Park, Jung-Won ❚ Univ. of Southern Cal. (USC) ❚ Center for Software Engineering (CSE) ❚ Systems Engineering Research Institute (SERI), Taejon, Korea ❚ December 29, 1997

What is COCOMO?
❚ COnstructive COst MOdel ❚ estimating software development effort and cost ❚ function of the size of the software product in source instructions ❚ function of the most significant software cost drivers

Importance of Software Cost Estimation - problems
❚ Software project personnel have no firm basis for telling a manager, customer, or salesperson that their proposed budget and schedule are unrealistic. ❚ Software analysts have no firm basis for making realistic hardware-software tradeoff analysis during the system design phase. ❚ Project managers have no firm basis for determining how much time and effort each software phase and activity should take.

USC-CSE Affiliates
❚ Commercial Industry (9) AT&T, Bellcore, EDS, HP, IDE, Motorola, Rational, TI, Xerox ❚ Aerospace Industry (9) E-Systems, Hughes, Litton, Lockheed, Loral, Northrop Grumman, Rockwell, SAIC, TRW ❚ Government (3) DISA, USAF Rome Lab, US Army Research Labs ❚ FFRDC’s and Consortia (5) Aerospace, IDA, JPL, SEI, SPC

Partial List of COCOMO Packages
❚ ❚ ❚ ❚ ❚ ❚ ❚ CB COCOMO COCOMOID COCOMO1 CoCoPro COSTAR COSTMODL GECOMO Plus ❚ ❚ ❚ ❚ GHL COCOMO REVIC SECOMO SWAN

Steps in Software Estimation
❚ 1. Establish Objectives * Rough Sizing * Make-or-Buy * Detailed Planning ❚ 2. Allocate enough time, dollars, talent ❚ 3. Pin down software requirements * Document definitions, assumptions ❚ 4. Work out as much as detail as objectives permit

Steps in Software Estimation
❚ 5. Use several independent techniques and sources * Top-Down vs. Bottom-Up * Algorithms vs. Expert Judgment ❚ 6. Compare and iterate estimates * Pin down and resolve inconsistencies * Be constructive ❚ 7. Follow up

COCOMO FEATURES
❚ ❚ ❚ ❚ ❚ ❚ ❚ Development cost and schedule estimates 3 levels of fidelity Macro and micro estimation Sensitivity analysis Software adaptation, conversion costs Software maintenance costs Calibration to user environment

Three COCOMO modes of Software Development
❚ Organic Mode - small software teams develop software in a highly familiar, in-house environment. ❚ Semidetached Mode - intermediate between the organic and embedded modes. ❚ Embedded Mode - need to operate within tight constraints. (electronic funds transfer system or an air traffic control system)

Basic COCOMO Effort and Schedule Equations
Mode Organic Embedded Effort MM = 2.4(KDSI)1.05 MM = 3.6(KDSI)1.20 Schedule TDEV = 2.5(MM)0.38 TDEV = 2.5(MM)0.35 TDEV = 2.5(MM)0.32

Semidetached MM = 3.0(KDSI)1.12

KDSI = thousands of delivered source instructions A COCOMO man-month consists of 152 hours of working time.

Intermediate COCOMO Effort and Schedule Equations
Mode Organic Embedded Nominal Effort Schedule

(MM)NOM = 3.2(KEDSI)1.05 TDEV = 2.5(MM)0.38 (MM)NOM = 2.8(KEDSI)1.20 TDEV = 2.5(MM)0.32

Semidetached (MM)NOM = 3.0(KEDSI)1.12 TDEV = 2.5(MM)0.35

MM = MMNOM x Π(EM) KEDSI = thousands of equivalent delivered source instructions

Software Productivity Range
1.20 1.23 1.23 Language experience Schedule constraint Data base size Turnaround time Virtual machine experience Virtual machine volatility Software tools Modern programming practices Storage constraint Applications experience Timing constraint Required reliability 2.36 1.00 Product complexity 4.18 Software productivity range Personnel/team capability 1.87

Software cost driver attribute

1.32 1.34

1.49 1.49 1.51 1.56 1.57

1.66

Software Costing and Sizing Accuracy vs. Phase
4x Completed Programs 2x + 1.5x Relative Size Range 1.25x x + + + + + + + USAF/ESD Proposals Size (DSI) + Cost ($)

+ + 0.5x

+ + +

0.25x

Concept of Operation

Rqts. Spec.

Product Design Spec.

Detail Design Spec.

Accepted Software

Feasability

Plans and Rqts.

Product Design Phases and Milestones

Detail Design

Devel. and Test

Software Cost Estimation Methods
❚ ❚ ❚ ❚ ❚ ❚ ❚ Algorithmic Model Expert Judgment Analogy Parkinson Price-To-Win Top-Down Bottom-Up

Software Cost Estimation Method -Algorithmic Model
❚ COCOMO II Strengths ❚ Objective, repeatable, analyzable formula ❚ Efficient, good for sensitivity analysis ❚ Objectively calibrated to experience

Weaknesses ❚ Subjective inputs ❚ Assessment of exceptional circumstances ❚ Calibrated to past, not future

Software Cost Estimation Method - Expert Judgment
Strengths ❚ Assessment of representativeness, interactions, exceptional circumstances Weaknesses ❚ No better than participants ❚ Biases, incomplete recall

Software Cost Estimation Method - Analogy
Strengths ❚ Based on representative experience Weaknesses ❚ Representativeness of experience

Software Cost Estimation Method - Parkinson
Strengths ❚ Correlates with some experience Weakness ❚ Reinforces poor practice

Software Cost Estimation Method - Price-To-Win
Strengths ❚ Often wins Weakness ❚ Generally produces large overruns

Software Cost Estimation Method - Top-Down
Strengths ❚ System level focus efficient Weakness ❚ Less detailed basis ❚ Less stable

Software Cost Estimation Method - Bottom-Up
Strengths ❚ More detailed basis ❚ More stable ❚ Fosters individual commitment Weakness ❚ May overlook system level costs ❚ Requires more effort

COCOMO Black Box Model
Software product size estimate Software product, process, computer, and personnel attributes Software reuse, maintenance, and increment parameters Software organization’s project data Software development, maintenance cost and schedule estimates

COCOMO II
Cost, schedule distribution by phase, activity, increment

COCOMO recalibrated to organization’s data

COCOMO II Objectives
❚ Develop a 1990’s-2000’s software cost model - Addressing new processes and practices ❚ Retain COCOMO internal, external openness ❚ Develop database, tool support for continuous model improvement ❚ Support closed-loop quantitative project management and process improvement

Future Software Marketplace Sector

User programming (55M performers in US in year 2005) Application generators (0.6M) Application composition (0.7M) Infrastructure (0.75M) System integration (0.7M)

COCOMO 2.0 Coverage of Future SW Practices Sectors
❚ User Programming: No need for cost model ❚ Applications Composition: Use object counts or object points - Count (weight) screens, reports, 3GL routines ❚ System Integration; development of applications generators and infrastructure software - Prototyping: Applications composition model - Early design: Function Points and 7 cost drivers - Post-architecture: Source Statements or Function Points and 17 cost drivers - Stronger reuse/reengineering model

Post-Architecture Model Formulation
Effort (person-months) = A x (Size)B x ΠEMi ❚ where A is a constant derived from calibration ❚ B = 1.01 + .01 x Σ SFi , where SFi is a weighting factor for ith scale driver (5) ❚ EMi is the effort multiplier for the ith cost driver (17)

Post-Architecture Model Formulation
❚ Effort:
           

PM = A×(Size)(SF) × ∏EM estimated i i

❚ Size - KSLOC (Thousands of Source Lines of Code) - UFP (Unadjusted Function Points) - EKSLOC (Equivalent KSLOC) used for adaptation ❚ SF: Scale Factors (5) ❚ EM: Effort Multipliers (7 for ED, 17 for PA)

Scale Factors
B = 1.01 + 0.01 x ΣSFi ❚ Precedentedness (PREC) ❚ Development flexibility (FLEX) ❚ Architecture/risk resolution (RESL) ❚ Team cohesion (TEAM) ❚ Process maturity(PMAT)

Scale Factors
Scale Very Low Factors (W i ) PREC thoroughly unprecedented FLEX rigorous RESL TEAM little (20%) very difficult interactions Low largely unprecedented occasional relaxation some (40%) Nominal somewhat unprecedented some relaxation often (60%) High generally familiar general conformity generally (75%) largely cooperative Very High largely familiar some conformity mostly (90%) highly cooperative Extra High thoroughly familiar general goals full (100%) seamless interactions

PMAT

some difficult basically interactions cooperative interaction weighted sum of KPA competition levels

• Precedentedness (PREC) • Development flexibility (FLEX) • Architecture/risk resolution (RESL) • Team cohesion (TEAM) • Process maturity(PMAT)

Post-Architecture EMs
❚ Product: RELY, DATA, DOCU, CPLX, RUSE ❚ Platform: TIME, STOR, PVOL ❚ Personnel: ACAP, AEXP, PCAP, PEXP, LTEX, PCON ❚ Project: TOOL, SITE, SCED

Effort Multipliers - Product
Required Reliability (RELY) Database Size (DATA) Complexity (CPLX) Required Reuse (RUSE) Documentation Match to Lifecycle (DOCU) Many life cycle needs uncovered Very Low slight incon venience Low low, easily recoverable losses DB bytes/ Pgm SLOC< 10 Nominal moderate, easily recoverable losses 10ŠD/P< 100 High high financial loss Very High risk to human life Extra High

100ŠD/P< 1000

D/P

1000

see Complexity Table none across project Right-sized to life-cycle needs across program Excessive for life-cycle needs across product line Very excessive for life-cycle needs across multiple product lines

Some life cycle needs uncovered

Effort Multipliers - Platform
Very Low Execution Time Constraint (TIME) Main Storage Constraint (STOR) Platform Volatility (PVOL) Low Nominal Š50% use of available execution time Š50% use of available storage major: 6 mo.; minor: 2 wk. High 70% Very High 85% Extra High 95%

70%

85%

95%

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

major: 2 mo.; minor: 1 wk.

major: 2 wk.; minor: 2 days

Effort Multipliers Personnel
Analyst Capability (ACAP) Programmer Capability (PCAP) Personnel Continuity (PCON) Application Experience (AEXP) Platform Experience (PEXP) Language and Tool Experience (LTEX) Very Low 15th percentile 15th percentile 48%/year Low 35th percentile 35th percentile 24%/year Nominal 55th percentile 55th percentile 12%/year High 75th percentile 75th percentile 6%/year Very High 90th percentile 90th percentile 3%/year Extra High

Š 2 months

6 months

1 year

3 years

6 years

Š 2 months

6 months

1 year

3 years

6 years

Š 2 months

6 months

1 year

3 years

6 years

Effort Multipliers - Project
Use of Software Tools (TOOL) Very Low edit, code, debug Low simple, frontend, backend CASE, little integration Multi-city and Multi company Individual phone, FAX Nominal basic lifecycle tools, moderately integrated Multi-city or Multi company Narrowband email High strong, mature lifecycle tools, moderately integrated Same city or metro. area Very High strong, mature, proactive lifecycle tools, well integrated with processes, methods, reuse Same building or complex Extra High

Multisite Development: Collocation (SITE) Multisite Development: Communicati ons (SITE) Required Development Schedule (SCED)

International

Fully collocated

Some phone, mail

75% of nominal

85%

100%

Wideband electronic communica tion 130%

-

Wideband elect. comm, occasional video conf. 160%

Interactive multimedia

Scale Factors
W(i) Precedentedness Development Flexibility Architecture / Risk Resolution Team Cohesion Process Maturity Very Low 4.05 6.07 4.22 4.94 4.54 Low 3.24 4.86 3.38 3.95 3.64 Nominal 2.43 3.64 2.53 2.97 2.73 High 1.62 2.43 1.69 1.98 1.82 Very High 0.81 1.21 0.84 0.99 0.91 Extra High 0.00 0.00 0.00 0.00 0.00

Cost Drivers
Cost Very Low RELY DATA CPLX RUSE DOCU TIME STOR PVOL ACAP PCAP PCON AEXP PEXP LTEX TOOL SITE SCED 1.50 1.37 1.24 1.22 1.25 1.22 1.24 1.25 1.29 0.87 1.22 1.16 1.10 1.10 1.12 1.10 1.12 1.10 1.10 0.89 0.75 0.75 Low 0.88 0.93 0.88 0.91 0.95 Rating Nominal 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 High 1.15 1.09 1.15 1.14 1.06 1.11 1.06 1.15 0.83 0.87 0.92 0.89 0.88 0.91 0.86 0.92 1.00 Very High 1.39 1.19 1.30 1.29 1.13 1.31 1.21 1.30 0.67 0.74 0.84 0.81 0.81 0.84 0.72 0.84 1.00 0.78 1.67 1.57 1.66 1.49 Extra High

Example - RELY
Required Software Reliability (RELY) ❚ Very Low : slight inconvenience ❚ Low : low, easily recoverable loses ❚ Nominal : moderate, easily recoverable loses ❚ High : high financial loss ❚ Very High : risk to human life

Effect of Required Reliability on Productivity
1.40 1.30 1.20 Low, easily recovered losses Low .90 .80 .70 1.10 High High financial loss Very High Risk to human life

Slight inconvenience Very Low

Size
❚ 1. SLOC ❚ 2. Function points - when little is known for SLOC estimation - function point quantify the amount of functionality in a software project ❚ 3. Adaptation - reuse model

COCOMO Reuse Model
❚ A nonlinear estimation model to convert adapted (reused or modified) software into equivalent size of new software ❚ AAF = 0.4(DM) + 0.3(CM) + 0.3(IM)
AAF : Adaptation Adjustment Factor DM : Percentage of Design Modified CM : Percentage of Code Modified IM : Percent of Integration Required for Modified Software

COCOMO Reuse Model
❚ ESLOC = ASLOC[AA+AAF(1+0.02(SU)(UNFM))] / 100, AAF 0.5 AAF : Adaptation Adjustment Factor ASLOC : Adapted Source Lines of Code ESLOC : Equivalent Source Lines of Code AA : Assessment and Assimilation effort SU : Software Understanding UNFM : Unfamiliarity

USC COCOMO Software Status
❚ There is a initial version available for MS Windows, SunOS, 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 and X is the version within that year)

Long Term Vision
❚ Produce model covering trade-offs among software cost, schedule, functionality, performance, quality ❚ Enable the use of the model to scope projects and monitor project progress ❚ Use the data from COCOMO-assisted management to calibrate improved versions of COCOMO ❚ Use the data and improved COCOMO cost drivers to enable affiliates to formulate and manage software improvement investments.

Data collection
❚ Defined the data needed (to completely describe the Post Architecture Model) ❚ Collected data with a paper form or a computer software tool ❚ Affiliate Organizations provided majority of data - Historical - whole project ❚ Site visits or phone interviews to record data ❚ Entered the data into the repository ❚ Did data consistency checking and conditioning

Information Sources
❚ E-mail: jungwon@usc.edu, cocomo-info@sunset.usc.edu ❚ URL:

http://sunset.usc.edu/COCOMOII/Cocomo.html

❚ Textbook: Barry Boehm, Software Engineering Economics, Prentice-Hall, 1981

COCOMO Glossary
❚ ❚ ❚ ❚ ❚ ❚ EAF : Effort Adjustment Factor NOM PM : Nominal Person-Month EST PM : Estimated Person-Month PROD : Productivity INST COST : Instruction cost (cost/SLOC) FSWP : Full-time SoftWare Personnel

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