1 / 31

Chapter 23

Chapter 23. Estimation. Software Engineering: A Practitioner’s Approach 6 th Edition Roger S. Pressman. Software Project Estimation (1). S/W is the most expensive element of virtually all computer based systems S/W cost and effort estimation will never be an exact science

petronella
Télécharger la présentation

Chapter 23

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Chapter 23 Estimation Software Engineering: A Practitioner’s Approach 6th Edition Roger S. Pressman

  2. Software Project Estimation (1) • S/W is the most expensive element of virtually all computer based systems • S/W cost and effort estimation will never be an exact science • Too many variables • Human • Technical • Environmental • Political

  3. Software Project Estimation (2) • Options for estimation • Delay estimation until late in the project • Attractive, but not practical • Base estimates on similar projects that have already been completed • Unfortunately, past experience has not always been a good indicator of future results • Use relatively simple decomposition techniques to generate project cost and effort estimates • “Divide and conquer” approach • Use one or more empirical models for software cost and effort estimation • Can be used as a cross-check for the previous option and vice versa

  4. Decomposition Techniques • Two different points of view for the decomposition approach • Decomposition of the problem • Decomposition of the process • But first, the project planner must • Understand the scope of the s/w to be built • Generate an estimate of its “size”

  5. Software Sizing (1) • The accuracy of a s/w project estimate is predicated on a number of things: • The degree to which the planner has properly estimated the size of the product to be built • The ability to translate the size estimate into human effort, calendar time, and dollars (required availability of past records) • The degree to which the project plan reflects the abilities of the s/w team • The stability of product requirements and the environment that supports the s/w engineering effort

  6. Software Sizing (2) • Sizing represents the project planner’s first major challenge • Size refers to a quantifiable outcome of the s/w project (e.g. LOC and/or FP) • Four different approaches to the sizing problem [PUT92] • “Fuzzy Logic” sizing • Function point sizing • Standard component sizing • Change sizing

  7. Problem-Based Estimation (1) • Example of baseline productivity metrics are LOC/pm or FP/pm • Making the use of single baseline productivity metric is discouraged • In general, LOC/pm or FP/pm averages should be computed by project domain • Local domain averages should be used

  8. Problem-Based Estimation (2) • Statistical approach – three-point or expected-value estimate • S = (sopt + 4sm + spess)/6 • S = expected-value for the estimation variable (size) • sopt = optimistic value • sm = most likely value • spess = pessimistic value

  9. An Example of LOC-Based Estimation (1)

  10. An Example of LOC-Based Estimation (2) • Estimated lines of code = W = 33,200 • Let, • Average productivity = 620 LOC/pm = X • Labor rate = $8,000 per month = Y • So, • Cost per line of code = Z = Y/X = $13 (approx.) • Total estimated project cost = W*Z = $431,000 (approx.) • Estimated effort = W/X = 54 person-months (approx)

  11. An Example of FP-Based Estimation (1) Figure 15.4: Computing function points

  12. An Example of FP-Based Estimation (2)

  13. An Example of FP-Based Estimation (3) Value Adjustment Factors

  14. An Example of FP-Based Estimation (4) • Now, • FPestimated = count-total  [0.65 + 0.01   (Fi)] • Fi (i = 1 to 14 are value adjustment factors) • So, • FPestimated = W = 320  [0.65 + 0.01  52] = 375 (approx.) • Let, • Average Productivity = X = 6.5 FP/pm • Labor rate = Y = $8,000 per month • So, • Cost per FP = Z = Y/X = $1,230 (approx.) • Total estimated project cost = W*Z = $461,000 (approx.) • Estimated effort = W/X = 58 person-months (approx)

  15. Empirical Estimation Models • Uses empirically derived formulas to predict effort as a function of LOC or FP • The empirical data are derived from a limited sample of projects • So, no estimation model is appropriate for all classes of s/w and in all development environments • The results obtained from such models must be used judiciously • An estimation model should be calibrated to reflect local conditions

  16. The Structure of Estimation Models (1) • Derived using regression analysis on data collected from past s/w projects • Overall structure, E = A + B (ev)c • Here, • A, B, and C are empirically derived constants • E is effort in person-months • ev is the estimation variable (either LOC or FP) • Some form of project adjustment component is also used

  17. The Structure of Estimation Models (2) • Example of a LOC-oriented estimation model (Bailey-Basili model) • E = 5.5 + 0.73  (KLOC)1.16 • Example of a FP-oriented estimation model (Kemerer model) • E = -37 + 0.96 FP • Estimation models must be calibrated for local needs.

  18. Summary of S/W estimation methods S/W equation TIME LOC/usecase Usecase based LOC $/LOC E(LOC) Problem based $/FP COST FP E(FP) Process based E(Effort) Man-month $/Man-month

  19. The COCOMO II Model (1) • COnstructive COst Model • A hierarchy of estimation models • Addresses the following areas • Application composition model • Early design stage model • Post-architecture stage model • Three different sizing options are available • Object points • Function points • Lines of source code

  20. The COCOMO II Model (2) • The COCOMO II application composition model uses object points • Object point is computed using counts of the number of • Screens (at the user interface) • Reports • Components likely to be required to build the application

  21. The COCOMO II Model (3) Figure 23.6: Complexity weighting for object types

  22. The COCOMO II Model (4) Figure 23.7: Productivity rate for object points

  23. The COCOMO II Model (5) • NOP = (object points)  [(100-%reuse)/100] • NOP = New Object Points • Object Points = Weighted Total • %reuse = Percent of reuse • Estimated effort = NOP/PROD • PROD = Productivity Rate • PROD = NOP/person-month

  24. The Software Equation [PUT92] (1) • It is a multivariate model • Has been derived from productivity data collected for over 4000 contemporary s/w projects • E = [LOC  B0.333/P]3 (1/t4) • Here, • E = effort in person-months or person-years • t = project duration in months or years • B = “special skills factor” • P = “productivity parameter”

  25. The Software Equation [PUT92] (2) • For small programs (KLOC = 5 to 15) • B = 0.16 • For programs greater than 70 KLOC • B = 0.39 • P = 2000 for development of real-time embedded s/w • P = 10,000 for telecommunication and systems s/w • P = 28,000 for business systems applications

  26. The Software Equation [PUT92] (3) • Simplified formulas • tmin = 8.14 (LOC/P)0.43 in months for tmin > 6 months • tmin = minimum development time • E = 180 Bt3 in person-months for E 20 person-months • Here t is represented in years

  27. The Make/Buy Decision • S/W acquisition options • s/w may be purchased (or licensed) off the shelf • “full-experience” or “partial-experience” s/w components may be acquired and then modified and integrated to meet specific needs • s/w may be custom built by an outside contractor to meet the purchaser’s specifications

  28. Creating a Decision Tree (1)

  29. Creating a Decision Tree (2) • The expected value for cost, computed along any branch of the decision tree, is • Expected cost =  (path probability)i  (estimated path cost)i • Where, i is the decision tree path

  30. Outsourcing • S/W engineering activities are contracted to a third party who does the work at lower cost and, hopefully, higher quality • S/W work conducted within a company is reduced to a contract management activity • The decision to outsource can be either strategic or tactical • Has both merits and demerits

  31. Chapter 23 • Introduction • 23.5 to 23.6.8 • 23.7 • 23.10 • Exercises • 23.1, 23.4—23.8, 23.12

More Related