![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](https://image.slidesharecdn.com/cocomomodel-250309152421-ac61aa38/85/software-project-management-cocomomodel-pdf-18-320.jpg)
![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](https://image.slidesharecdn.com/cocomomodel-250309152421-ac61aa38/85/software-project-management-cocomomodel-pdf-19-320.jpg)
software project management cocomomodel.pdf
- 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. COCOMO Models COCOMO has three different models that reflect the complexity: the Basic Model the Intermediate Model and the Detailed Model
- 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. Contd… Embedded Mode • Software projects that must be developed within a set of tight hardware, software, and operational constraints.
- 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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