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Software cost estimation

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Software cost estimation

  1. 1. SOFTWARE COST AND SCHEDULE ESTIMATION Presented By: Deep kumar sharma Mtech(1st sem) 1
  2. 2. TOPICS COVERED 1. Software Cost components 2. Software productivity 3. Productivity measures 4. Measurement problems 5. Estimation techniques 6. Project scheduling 7. References 2
  3. 3. 1.SOFTWARE COST COMPONENTS  Travel and training costs  Effort costs (the dominant factor in most projects)  The salaries of engineers involved in the project  Social and insurance costs  Effort costs must take overheads into account  Costs of building, heating, lighting  Costs of networking and communications  Costs of shared facilities (eg. library, staff restaurant, etc.)  Hardware and software costs 3
  4. 4. 2.SOFTWARE PRODUCTIVITY  A measure of the rate at which individual engineers involved in software development produce software and associated documentation  Not quality-oriented although quality assurance is a factor in productivity assessment  Essentially, we want to measure useful functionality produced per time unit 4
  5. 5. 3.PRODUCTIVITY MEASURES  Function-related measures based on an estimate of the functionality of the delivered software. Function- points are the best known of this type of measure  Size related measures based on some output from the software process. This may be lines of delivered source code, object code instructions, etc 5
  6. 6. 4.MEASUREMENT PROBLEMS  Estimating the size of the measure (e.g. how many function points)  Estimating the total number of programmer months that have elapsed  Estimating contractor productivity (e.g. documentation team) and incorporating this estimate in overall estimate 6
  7. 7. 5.ESTIMATION TECHNIQUES 5.1. SOURCE LINES OF CODE 5.2. FUNCTION POINT ANALYSIS 7
  8. 8. 5.1.SOURCE LINES OF CODE  LOC is a software metric used to measure the size of a computer program by counting the number of lines in the text of the program's source code  LOC is typically used to predict the amount of effort that will be required to develop a program, as well as to estimate programming productivity or maintainability once the software is produced  This model assumes that there is a linear relationship between system size and volume of documentation 8
  9. 9. 5.1.1.PRODUCTIVITY COMPARISONS  The lower level the language, the more productive the programmer  The same functionality takes more code to implement in a lower- level language than in a high-level language  The more verbose the programmer, the higher the productivity  Measures of productivity based on lines of code suggest that programmers who write verbose code are more productive than programmers who write compact code 9
  10. 10. 5.1.2.SYSTEM DEVELOPMENT TIMES Analysis Design Coding Testing Documentation Assembly code High-level language 3 weeks 3 weeks 5 weeks 5 weeks 8 weeks 4 weeks 10 weeks 6 weeks 2 weeks 2 weeks Size Effort Productivity Assembly code High-level language 5000 lines 1500 lines 28 weeks 20 weeks 714 lines/month 300 lines/month 10
  11. 11. 5.2.FUNCTION POINT ANALYSIS  Function point metric is that it can be used to easily estimate the size of a software product directly from the problem specification  The idea underlying the FP metric is that the size of a software product is directly dependent on the no. of different function or features it support  The function point analysis measure quantities the functionality requested and provided to the user based on the user’s requirements and high level logical design 11
  12. 12.  Working from the project design specifications, the following system functions are measured (counted):  Inputs  Outputs  Files  Inquires  Interfaces  A weight is associated with each of these and the function point count is computed by multiplying each raw count by the weight and summing all values UFP=∑∑ Zij Wij 12
  13. 13.  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 13
  14. 14. 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 14
  15. 15. Adjustment factor 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 15
  16. 16.  The function point count is modified by complexity of the project  FPs can be used to estimate LOC depending on the average number of LOC per FP for a given language  FP = UFP * CAF UFP is unadjusted function point CAF is complexity adjustment factor  CAF= 0.65 + 0.01 * ∑ fi 16
  17. 17. 6.PROJECT SCHEDULING  Split project into tasks (= create a WBS)  Estimate time and resources required to complete each task  Organize tasks concurrently to make optimal use of workforce  Minimize task dependencies to avoid delays caused by one task waiting for another to complete  Dependent on project managers intuition and experience 17
  18. 18.  Once tasks (from the WBS) and size/effort (from estimation) are known: then schedule  Primary objectives • Best time • Least cost • Least risk  Secondary objectives • Evaluation of schedule alternatives • Effective use of resources • Communications 18
  19. 19. 8.REFERENCES  http://www.cfm.va.gov/til/dManual/dmCost.pdf  http://doit.maryland.gov/SDLC/Documents/Cost_Estimating .pdf  http://www.efcog.org/wg/pm_ce/docs/OMBE_Guidelines. pdf  http://www.infosys.com/infosys- labs/publications/Documents/practical-software- estimation.pdf 19
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