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The Constructive Cost Model (COCOMO) is a procedural software cost estimation model developed by Barry W. Boehm. The model parameters are derived from fitting a regression formula using data from historical projects (61 projects for COCOMO 81 and 163 projects for COCOMO II).
The constructive cost model was developed by Barry W. Boehm in the late 1970s and published in Boehm's 1981 book Software Engineering Economics as a model for estimating effort, cost, and schedule for software projects. It drew on a study of 63 projects at TRW Aerospace where Boehm was Director of Software Research and Technology. The study examined projects ranging in size from 2,000 to 100,000 lines of code, and programming languages ranging from assembly to PL/I. These projects were based on the waterfall model of software development which was the prevalent software development process in 1981.
References to this model typically call it COCOMO 81. In 1995 COCOMO II was developed and finally published in 2000 in the book Software Cost Estimation with COCOMO II. COCOMO II is the successor of COCOMO 81 and is claimed to be better suited for estimating modern software development projects; providing support for more recent software development processes and was tuned using a larger database of 161 projects. The need for the new model came as software development technology moved from mainframe and overnight batch processing to desktop development, code reusability, and the use of off-the-shelf software components.
COCOMO consists of a hierarchy of three increasingly detailed and accurate forms. The first level, Basic COCOMO is good for quick, early, rough order of magnitude estimates of software costs, but its accuracy is limited due to its lack of factors to account for difference in project attributes (Cost Drivers). Intermediate COCOMO takes these Cost Drivers into account and Detailed COCOMO additionally accounts for the influence of individual project phases.
Intermediate COCOMO computes software development effort as function of program size and a set of "cost drivers" that include subjective assessment of product, hardware, personnel and project attributes. This extension considers a set of four "cost drivers", each with a number of subsidiary attributes:-
- Product attributes
- Required software reliability extent
- Size of application database
- Complexity of the product
- Hardware attributes
- Run-time performance constraints
- Memory constraints
- Volatility of the virtual machine environment
- Required turnabout time
- Personnel attributes
- Analyst capability
- Software engineering capability
- Applications experience
- Virtual machine experience
- Programming language experience
- Project attributes
- Use of software tools
- Application of software engineering methods
- Required development schedule
Each of the 15 attributes receives a rating on a six-point scale that ranges from "very low" to "extra high" (in importance or value). An effort multiplier from the table below applies to the rating. The product of all effort multipliers results in an effort adjustment factor (EAF). Typical values for EAF range from 0.9 to 1.4.
|Very Low||Low||Nominal||High||Very High||Extra High|
|Required software reliability||0.75||0.88||1.00||1.15||1.40|
|Size of application database||0.94||1.00||1.08||1.16|
|Complexity of the product||0.70||0.85||1.00||1.15||1.30||1.65|
|Run-time performance constraints||1.00||1.11||1.30||1.66|
|Volatility of the virtual machine environment||0.87||1.00||1.15||1.30|
|Required turnabout time||0.87||1.00||1.07||1.15|
|Software engineer capability||1.42||1.17||1.00||0.86||0.70|
|Virtual machine experience||1.21||1.10||1.00||0.90|
|Programming language experience||1.14||1.07||1.00||0.95|
|Application of software engineering methods||1.24||1.10||1.00||0.91||0.82|
|Use of software tools||1.24||1.10||1.00||0.91||0.83|
|Required development schedule||1.23||1.08||1.00||1.04||1.10|
The Intermediate Cocomo formula now takes the form:
where E is the effort applied in person-months, KLoC is the estimated number of thousands of delivered lines of code for the project, and EAF is the factor calculated above. The coefficient ai and the exponent bi are given in the next table.
Software project ai bi Organic 3.2 1.05 Semi-detached 3.0 1.12 Embedded 2.8 1.20
The Development time D calculation uses E in the same way as in the Basic COCOMO.
- Comparison of development estimation software
- Cost overrun
- Estimation in software engineering
- Function point
- Object point
- Putnam model
- Software development effort estimation
- Software engineering economics
- Stutzke, Richard. "Software Estimating Technology: A Survey". Retrieved 9 Oct 2016.DOC
- Boehm, Barry (1981). Software Engineering Economics. Prentice-Hall. ISBN 0-13-822122-7.
- Barry Boehm, Chris Abts, A. Winsor Brown, Sunita Chulani, Bradford K. Clark, Ellis Horowitz, Ray Madachy, Donald J. Reifer, and Bert Steece. Software Cost Estimation with COCOMO II (with CD-ROM). Englewood Cliffs, NJ:Prentice-Hall, 2000. ISBN 0-13-026692-2
- Kemerer, Chris F. (May 1987). "An Empirical Validation of Software Cost Estimation Models" (PDF). Communications of the ACM. 30 (5): 416–42. doi:10.1145/22899.22906.
- COCOMO 81 data on tera-PROMISE
- Analysis of the COCOMO 81 data obtains a different value for the Organic exponent.