These concerns have led to the formulation of a new version of the Constructive Cost Model (COCOMO) for software. Users in a recent Software Engineering.

  1. Cocomo Model Pdf
  2. Cocomo Model In Software Engineering Tutorial Point
  1. Amadeus (1994),Amadeus Measurement System User's Guide, Version 2.3a, Amadeus Software Research, Inc., Irvine, CA.Google Scholar
  2. Banker, R., R. Kauffman, and R. Kumar (1994), “An Empirical Test of Object-Based Output Measurement Metrics in a Computer Aided Software Engineering (CASE) Environment”,Journal of Management Information Systems, to appear.Google Scholar
  3. Banker, R., H. Chang, and C. Kemerer (1994a), “Evidence on Economics of Scale in Software Development”,Information and Software Technology, to appear.Google Scholar
  4. Behrens, C. (1983), “Measuring the Productivity of Computer Systems Development Activities with Function Points”,IEEE Transactions on Software Engineering, November.Google Scholar
  5. Boehm, B. (1981),Software Engineering Economics, Prentice-Hall.Google Scholar
  6. Boehm, B. (1983), “The Hardware/Software Cost Ratio: Is It a Myth?”Computer 16, 3, pp. 78–80.Google Scholar
  7. Boehm, B. (1985), “COCOMO: Answering the Most Frequent Questions”, InProceedings, First COCOMO Users' Group Meeting, Wang Institute, Tyngsboro, MA.Google Scholar
  8. Boehm, B. (1989),Software Risk Management, IEEE Computer Society Press, Los Alamitos, CA.Google Scholar
  9. Boehm, B., T. Gray, and T. Seewaldt (1984), “Prototyping vs. Specifying: A Multi-Project Experiment”,IEEE Transactions on Software Engineering, May, 133–145.Google Scholar
  10. Boehm, B., and W. Royce (1989), “Ada COCOMO and the Ada Process Model”,Proceedings, Fifth COCOMO Users' Group Meeting, Software Engineering Institute, Pittsburgh, PA.Google Scholar
  11. Chidamber, S., and C. Kemerer (1994), “A Metrics Suite for Object Oriented Design”,IEEE Transactions on Software Engineering, to appear.Google Scholar
  12. Computer Science and Telecommunications Board (CSTB) National Research Council (1993),Computing Professionals: Changing Needs for the 1990's, National Academy Press, Washington, DC.Google Scholar
  13. Devenny, T. (1976), “An Exploratory Study of Software Cost Estimating at the Electronic Systems Division”, Thesis No. GSM/SM/765-4, Air Force Institute of Technology, Dayton, OH.Google Scholar
  14. Gerlich, R., and U. Denskat (1994), “A Cost Estimation Model for Maintenance and High Reuse”,Proceedings, ESCOM 1994, Ivrea, Italy.Google Scholar
  15. Goethert, W., E. Bailey, and M. Busby (1992), “Software Effort and Schedule Measurement: A Framework for Counting Staff Hours and Reporting Schedule Information”, CMU/SEI-92-TR-21, Software Engineering Institute, Pittsburgh, PA.Google Scholar
  16. Goudy, R. (1987), “COCOMO-Based Personnel Requirements Model”,Proceedings, Third COCOMO Users' Group Meeting, Software Engineering Institute, Pittsburgh, PA.Google Scholar
  17. IFPUG (1994),IFPUG Function Point Counting Practices: Manual Release 4.0, International Function Point Users' Group, Westerville, OH.Google Scholar
  18. Kauffman, R. and R. Kumar (1993), “Modeling Estimation Expertise in Object Based ICASE Environments”, Stern School of Business Report, New York University.Google Scholar
  19. Kemerer, C. (1987), “An Empirical Validation of Software Cost Estimation Models”,Communications of the ACM, 416–429.Google Scholar
  20. Kominski, R. (1991),Computer Use in the United States: 1989, Current Population Reports, Series P-23, No. 171, U.S. Bureau of the Census, Washington, DC.Google Scholar
  21. Kunkler, J. (1983), “A Cooperative Industry Study on Software Development/Maintenance Productivity”, Xerox Corporation, Xerox Square — XRX2 52A, Rochester, NY 14644, Third Report.Google Scholar
  22. Miyazaki, Y. and K. Mori (1985), “COCOMO Evaluation and Tailoring”,Proceedings, ICSE 8, IEEE-ACM-BCS, London, pp. 292–299.Google Scholar
  23. Parikh, G. and N. Zvegintzov (1983), “The World of Software Maintenance”,Tutorial on Software Maintenance, IEEE Computer Society Press, pp. 1–3.Google Scholar
  24. Park, R. (1992), “Software Size Measurement: A Framework for Counting Source Statements”, CMU/SEI-92-TR-20, Software Engineering Institute, Pittsburgh, PA.Google Scholar
  25. Park, R., W. Goethert, and J. Webb (1994), “Software Cost and Schedule Estimating: A Process Improvement Initiative”, CMU/SEI-94-TR-03, Software Engineering Institute, Pittsburgh, PA.Google Scholar
  26. Paulk, M., B. Curtis, M. Chrissis, and C. Weber (1993), Capability Maturity Model for Software, Version 1.1”, CMU/SEI-93-TR-24, Software Engineering Institute, Pittsburgh, PA.Google Scholar
  27. Pfleeger, S. (1991), “Model of Software Effort and Productivity”,Information and Software Technology 33, 3, 224–231.Google Scholar
  28. Royce, W. (1990), “TRW's Ada Process Model for Incremental Development of Large Software Systems,Proceedings, ICSE 12, Nice, France.Google Scholar
  29. Ruhl, M. and M. Gunn (1991), “Software Reengineering: A Case Study and Lessons Learned”, NIST Special Publication 500-193, Washington, DC.Google Scholar
  30. Selby, R. (1988), “Empirically Analyzing Software Reuse in a Production Environment”, InSoftware Reuse: Emerging Technology, W. Tracz, Ed., IEEE Computer Society Press, pp. 176–189.Google Scholar
  31. Selby, R., A Porter, D. Schmidt, and J. Berney (1991), “Metric-Driven Analysis and Feedback Systems for Enabling Empirically Guided Software Development”,Proceedings of the Thirteenth International Conference on Software Engineering (ICSE 13), Austin, TX, pp. 288–298.Google Scholar
  32. Silvestri, G. and J. Lukasiewicz (1991), “Occupational Employment Projections”,Monthly Labor Review 114, 11, 64–94.Google Scholar
  33. SPR (1993), “Checkpoint User's Guide for the Evaluator”, Software Productivity Research, Inc., Burlington, MA.Google Scholar
Jump to navigationJump to search

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).


History[edit]

The constructive cost model was developed by Barry W. Foresee garage door opener manual. Boehm in the late 1970s[1] and published in Boehm's 1981 book Software Engineering Economics[2] 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.[3] 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.Last one is Complete COCOMO model which is short coming of both basic & intermediate.

Intermediate COCOMOs[edit]

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

Cocomo Model Pdf

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.

Cost DriversRatings
Very LowLowNominalHighVery HighExtra High
Product attributes
Required software reliability0.750.881.001.151.40
Size of application database0.941.001.081.16
Complexity of the product0.700.851.001.151.301.65
Hardware attributes
Run-time performance constraints1.001.111.301.66
Memory constraints1.001.061.211.56
Volatility of the virtual machine environment0.871.001.151.30
Required turnabout time0.871.001.071.15
Personnel attributes
Analyst capability1.461.191.000.860.71
Applications experience1.291.131.000.910.82
Software engineer capability1.421.171.000.860.70
Virtual machine experience1.211.101.000.90
Programming language experience1.141.071.000.95
Project attributes
Application of software engineering methods1.241.101.000.910.82
Use of software tools1.241.101.000.910.83
Required development schedule1.231.081.001.041.10

The Intermediate Cocomo formula now takes the form:

E=ai(KLoC)(bi)(EAF)

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 projectaibi
Organic3.21.05
Semi-detached3.01.12
Embedded2.81.20

Cocomo Model In Software Engineering Tutorial Point

The Development time D calculation uses E in the same way as in the Basic COCOMO.

Sanitaire

Cocomo Model In Software Engineering Tutorial Point

See also[edit]

References[edit]

  1. ^Stutzke, Richard. 'Software Estimating Technology: A Survey'. Retrieved 9 Oct 2016.DOC
  2. ^Boehm, Barry (1981). Software Engineering Economics. Prentice-Hall. ISBN0-13-822122-7.
  3. ^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. ISBN0-13-026692-2

Further reading[edit]

  • 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.

External links[edit]

  • COCOMO 81 data on tera-PROMISE
  • Analysis of the COCOMO 81 data obtains a different value for the Organic exponent.
Cocomo Model In Software Engineering Tutorial Point


Retrieved from 'https://en.wikipedia.org/w/index.php?title=COCOMO&oldid=897008190'
Hidden categories: