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Cocomo model

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Cocomo model

  1. 1. COCOMO Model Basic
  2. 2. Introduction COCOMO is one of the most widely used software estimation models in the world It was developed by Barry Boehm in 1981 COCOMO predicts the effort and schedule for a software product development based on inputs relating to the size of the software and a number of cost drivers that affect productivity
  3. 3. COCOMO Models COCOMO has three different models that reflect the complexity: the Basic Model the Intermediate Model and the Detailed Model
  4. 4. The Development Modes: Project Characteristics Organic Mode • Relatively small, simple software projects • Small teams with good application experience work to a set of less than rigid requirements • Similar to the previously developed projects • relatively small and requires little innovation Semidetached Mode • Intermediate (in size and complexity) software projects in which teams with mixed experience levels must meet a mix of rigid and less than rigid requirements.
  5. 5. Contd… Embedded Mode • Software projects that must be developed within a set of tight hardware, software, and operational constraints.
  6. 6. COCOMO: Some Assumptions Primary cost driver is the number of Delivered Source Instructions (DSI) / Delivered Line Of Code developed by the project COCOMO estimates assume that the project will enjoy good management by both the developer and the customer Assumes the requirements specification is not substantially changed after the plans and requirements phase
  7. 7. Basic COCOMO Basic COCOMO is good for quick, early, rough order of magnitude estimates of software costs It does not account for differences in hardware constraints, personnel quality and experience, use of modern tools and techniques, and other project attributes known to have a significant influence on software costs, which limits its accuracy
  8. 8. Basic COCOMO Model: Formula E=ab (KLOC or KDSI) b b D=cb(E) d b P=E/D where E is the effort applied in person-months, D is the development time in chronological months, KLOC / KDSI is the estimated number of delivered lines of code for the project (expressed in thousands), and P is the number of people required. The coefficients ab, bb, cb and db are given in next slide.
  9. 9. Contd… Software project ab bb cb db Organic 2.4 1.05 2.5 0.38 Semi-detached 3.0 1.12 2.5 0.35 Embedded 3.6 1.20 2.5 0.32
  10. 10. Basic COCOMO Model: Equation Mode Effort Schedule Organic E=2.4*(KDSI) 1.05 TDEV=2.5*(E) 0.38 Semidetached E=3.0*(KDSI) 1.12 TDEV=2.5*(E) 0.35 Embedded E=3.6*(KDSI) 1.20 TDEV=2.5*(E) 0.32
  11. 11. Basic COCOMO Model: Limitation Its accuracy is necessarily limited because of its lack of factors which have a significant influence on software costs The Basic COCOMO estimates are within a factor of 1.3 only 29% of the time, and within a factor of 2 only 60% of the time
  12. 12. Basic COCOMO Model: Example  We have determined our project fits the characteristics of Semi-Detached mode  We estimate our project will have 32,000 Delivered Source Instructions. Using the formulas, we can estimate:  Effort = 3.0*(32) 1.12 = 146 man-months  Schedule = 2.5*(146) 0.35 = 14 months  Productivity = 32,000 DSI / 146 MM = 219 DSI/MM  Average Staffing = 146 MM /14 months = 10 FSP
  13. 13. Function Point Analysis What is Function Point Analysis (FPA)?  It is designed to estimate and measure the time, and thereby the cost, of developing new software applications and maintaining existing software applications.  It is also useful in comparing and highlighting opportunities for productivity improvements in software development.  It was developed by A.J. Albrecht of the IBM Corporation in the early 1980s.  The main other approach used for measuring the size, and therefore the time required, of software project is lines of code (LOC) – which has a number of inherent problems.
  14. 14. Function Point Analysis How is Function Point Analysis done? Working from the project design specifications, the following system functions are measured (counted):  Inputs  Outputs  Files  Inquires  Interfaces
  15. 15. Function Point Analysis These function-point counts are then weighed (multiplied) by their degree of complexity: Simple Average Complex Inputs 2 4 6 Outputs 3 5 7 Files 5 10 15 Inquires 2 4 6 Interfaces 4 7 10
  16. 16. Function Point Analysis A simple example: inputs 3 simple X 2 = 6 4 average X 4 = 16 1 complex X 6 = 6 outputs 6 average X 5 = 30 2 complex X 7 = 14 files 5 complex X 15 = 75 inquiries 8 average X 4 = 32 interfaces 3 average X 7 = 21 4 complex X 10 = 40 Unadjusted function points 240
  17. 17. Function Point Analysis In addition to these individually weighted function points, there are factors that affect the project and/or system as a whole. There are a number (~35) of these factors that affect the size of the project effort, and each is ranked from “0”- no influence to “5”- essential. The following are some examples of these factors:  Is high performance critical?  Is the internal processing complex?  Is the system to be used in multiple sites and/or by multiple organizations?  Is the code designed to be reusable?  Is the processing to be distributed?  and so forth . . .
  18. 18. Function Point Analysis Continuing our example . . . Complex internal processing = 3 Code to be reusable = 2 High performance = 4 Multiple sites = 3 Distributed processing = 5 Project adjustment factor = 17 Adjustment calculation: Adjusted FP = Unadjusted FP X [0.65 + (adjustment factor X 0.01)] = 240 X [0.65 + ( 17 X 0.01)] = 240 X [0.82] = 197 Adjusted function points
  19. 19. Function Point Analysis But how long will the project take and how much will it cost? As previously measured, programmers in our organization average 18 function points per month. Thus . . . 197 FP divided by 18 = 11 man-months If the average programmer is paid $5,200 per month (including benefits), then the [labor] cost of the project will be . . . 11 man-months X $5,200 = $57,200
  20. 20. Function Point Analysis Because function point analysis is independent of language used, development platform, etc. it can be used to identify the productivity benefits of . . . One programming language over another One development platform over another One development methodology over another One programming department over another Before-and-after gains in investing in programmer training And so forth . . .
  21. 21. Function Point Analysis But there are problems and criticisms:  Function point counts are affected by project size  Difficult to apply to massively distributed systems or to systems with very complex internal processing  Difficult to define logical files from physical files  The validity of the weights that Albrecht established – and the consistency of their application – has been challenged  Different companies will calculate function points slightly different, making intercompany comparisons questionable

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