1. Lecture 5 Estimation-revised Estimate size, then Estimate effort, schedule and cost from size CS 551

2. Project Metrics l Cost and schedule estimation l Measure progress l Calibrate models for future estimating l Metric/Scope Manager Product Number of projects x number of metrics = 15-20

3. Approaches to Cost Estimtation By expert By analogies Decomposition Parkinson’s Law; work expands to fill time available Pricing to win: price is set at customer willingness to pay Lines of Code Function Points Mathematical Models: Function Points & COCOMO

4. Time Staff-month T theoretical 75% * T theoretical Impossible design Linear increase Boehm: “A project can not be done in less than 75% of theoretical time” T theoretical = 2.5 * 3 √staff-months But, how can I estimate staff months?

5. Sizing Software Projects l Effort = (productivity) -1 (size) c productivity ≡ staff-months/kloc size ≡ kloc Staff months Lines of Code or Function Points 500

6. Understanding the equations Consider a transaction project of 38,000 lines of code, what is the shortest time it will take to develop? Module development is about 400 SLOC/staff month Effort = (productivity) -1 (size) c = (1/.400 KSLOC/SM) (38 KSLOC) 1.02 = 2.5 (38) 1.02 ≈ 100 SM Min time =.75 T= (.75)(2.5)(SM) 1/3 ≈ 1.875(100) 1/3 ≈ 1.875 x 4.63 ≈ 9 months

7. How many software engineers? l 1 full time staff week = 40 hours, 1 student week = 20 hours. l Therefore, our estimation of 100 staff months is actually 200 student months. l The period of coding is December 2004 through April 2005, which is a period of 5 months. l 200 staff months/5 months = 40 student software engineers, therefore simplification is mandatory As of 8Nov07

8. Function Points Bell Laboratories data Capers Jones data Productivity (Function points / staff month) Productivity= f (size)

9. Average Change Processing Time: for two Systems of Systems Average workdays to process changes Thanks to Barry Boehm

10. Effect of ignoring software structure Software risks discovered too late Slow, buggy change management SW Software SW (WBS-based) Thanks to Barry Boehm

11. Software Development Schedule Trends Number of Years ≈ 0.04 * cube root (NCKSLOC) Years to Develop Software, Hardware HW Thousands of source lines of code (KSLOC) SW

12. 05/22/2007(c) USC-CSSE12 The Cone of Uncertainty: Usual result of total commitment ^ Inadequate PDR Better to buy information to reduce risk Thanks to Barry Boehm

13. 05/22/2007(c) USC-CSSE13 There is Another Cone of Uncertainty: Shorter increments are better Uncertainties in competition, technology, organizations, mission priorities

14. The Incremental Commitment Life Cycle Process: Overview Stage I: DefinitionStage II: Development and Operations Anchor Point Milestones Concurrently engr. OpCon, rqts, arch, plans, prototypes Concurrently engr. Incr.N (ops), N+1 (devel), N+2 (arch)

15. ICM Stage II: Increment View

16. Increment View Thanks to Barry Boehm

17. V C R D C R I O C O C R A C R C C D RUP/ICM Anchor Points Enable Concurrent Engineering Thanks to Barry Boehm

18. Lines of Code l LOC ≡ Line of Code l KLOC ≡ Thousands of LOC l KSLOC ≡ Thousands of Source LOC l NCSLOC ≡ New or Changed KSLOC

19. Productivity per staff-month: »50 NCSLOC for OS code (or real-time system) »250-500 NCSLOC for intermediary applications (high risk, on-line) »500-1000 NCSLOC for normal applications (low risk, on- line) »10,000 – 20,000 NCSLOC for reused code Reuse note: Sometimes, reusing code that does not provide the exact functionality needed can be achieved by reformatting input/output. This decreases performance but dramatically shortens development time. Bernstein’s rule of thumb

20. Productivity: Measured in 2000 Classical rates130 – 195 NCSLOC Evolutionary or Incremental approaches 244 – 325 NCSLOC New embedded flight software 17 – 105 NCSLOC

21. QSE Lambda Protocol l Prospectus l Measurable Operational Value l Prototyping or Modeling l sQFD l Schedule, Staffing, Quality Estimates l ICED-T l Trade-off Analysis

22. Heuristics for requirements engineering l Move some of the desired functionality into version 2 l Deliver product in stages 0.2, 0.4… l Eliminate features l Simplify Features l Reduce Gold Plating l Relax the specific feature specificaitons

23. Function Point (FP) Analysis l Useful during requirement phase l Substantial data supports the methodology l Software skills and project characteristics are accounted for in the Adjusted Function Points l FP is technology and project process dependent so that technology changes require recalibration of project models. l Converting Unadjusted FPs (UFP) to LOC for a specific language (technology) and then use a model such as COCOMO.

24. Function Point Calculations l Unadjusted Function Points UFP= 4I + 5O + 4E + 10L + 7F, Where I ≡ Count of input types that are user inputs and change data structures. O ≡ Count of output types E ≡ Count of inquiry types or inputs controlling execution.  [think menu selections] L ≡ Count of logical internal files, internal data used by system  [think index files; they are group of logically related data entirely within the applications boundary and maintained by external inputs. ] F ≡ Count of interfaces data output or shared with another application Note that the constants in the nominal equation can be calibrated to a specific software product line.

25. Complexity Table TYPE:SIMPLEAVERAGECOMPLEX INPUT (I)346 OUTPUT(O)457 INQUIRY(E)346 LOG INT (L)71015 INTERFACES (F) 5710

26. Complexity Factors 1. Problem Domain___ 2. Architecture Complexity ___ 3. Logic Design -Data ___ 4. Logic Design- Code___ Total ___ Complexity = Total/4 = _________

27. Problem Domain Measure of Complexity (1 is simple and 5 is complex) 1. All algorithms and calculations are simple. 2. Most algorithms and calculations are simple. 3.Most algorithms and calculations are moderately complex. 4.Some algorithms and calculations are difficult. 5.Many algorithms and calculations are difficult. Score ____

28. Architecture Complexity Measure of Complexity (1 is simple and 5 is complex) 1. Code ported from one known environment to another. Application does not change more than 5%. 2. Architecture follows an existing pattern. Process design is straightforward. No complex hardware/software interfaces. 3. Architecture created from scratch. Process design is straightforward. No complex hardware/software interfaces. 4. Architecture created from scratch. Process design is complex. Complex hardware/software interfaces exist but they are well defined and unchanging. 5. Architecture created from scratch. Process design is complex. Complex hardware/software interfaces are ill defined and changing. Score ____

29. Logic Design -Data 1.Simple well defined and unchanging data structures. Shallow inheritance in class structures. No object classes have inheritance greater than 3. 2.Several data element types with straightforward relationships. No object classes have inheritance greater than 3.Multiple data files, complex data relationships, many libraries, large object library. No more than ten percent of the object classes have inheritance greater than three. The number of object classes is less than 1% of the function points 4.Complex data elements, parameter passing module-to-module, complex data relationships and many object classes has inheritance greater than three. A large but stable number of object classes. 5.Complex data elements, parameter passing module-to-module, complex data relationships and many object classes has inheritance greater than three. A large and growing number of object classes. No attempt to normalize data between modules Score ____

30. Logic Design- Code 1.Nonprocedural code (4GL, generated code, screen skeletons). High cohesion. Programs inspected. Module size constrained between 50 and 500 Source Lines of Code (SLOCs). 2.Program skeletons or patterns used. ). High cohesion. Programs inspected. Module size constrained between 50 and 500 SLOCs. Reused modules. Commercial object libraries relied on. High cohesion. 3.Well-structured, small modules with low coupling. Object class methods well focused and generalized. Modules with single entry and exit points. Programs reviewed. 4.Complex but known structure randomly sized modules. Some complex object classes. Error paths unknown. High coupling. 5.Code structure unknown, randomly sized modules, complex object classes and error paths unknown. High coupling. Score __

31. Complexity Factors 1. Problem Domain___ 2. Architecture Complexity ___ 3. Logic Design -Data ___ 4. Logic Design- Code___ Total ___ Complexity = Total/4 = _________

32. Computing Function Points See http://www.engin.umd.umich.edu/CIS/course.des/cis525/js/f00/artan/f unctionpoints.htm

33. Function Points Qualifiers l Based on counting data structures l Focus is on-line data base systems l Less accurate for WEB applications l Even less accurate for Games, finite state machine and algorithm software l Not useful for extended machine software and compliers An alternative to NCKSLOC because estimates can be based on requirements and design data.

34. Initial Conversion LanguageMedian SLOC/function point C104 C++53 HTML42 JAVA59 Perl60 J2EE50 Visual Basic42 http://www.qsm.com/FPGearing.html

37. SLOC l Function Points = UFP x TCF = 78 *.96 = 51.84 ~ 52 function points l 78 UFP * 53 (C++ )SLOC / UFP = 4,134 SLOC = 4.158 KSLOC. (Reference for SLOC per function point: http://www.qsm.com/FPGearing.html )

38. Function Point pros and cons l Pros: Language independent Understandable by client Simple modeling Hard to fudge Visible feature creep l Cons: Labor intensive Extensive training Inexperience results in inconsistent results Weighted to file manipulation and transactions Systematic error introduced by single person, multiple raters advised

39. Heuristics to do Better Estimates l Decompose Work Breakdown Structure to lowest possible level and type of software. l Review assumptions with all stakeholders l Do your homework - past organizational experience l Retain contact with developers l Update estimates and track new projections (and warn) l Use multiple methods l Reuse makes it easier (and more difficult) l Use ‘current estimate’ scheme

40. Specification for Development Plan l Project l Feature List l Development Process l Size Estimates l Staff Estimates l Schedule Estimates l Organization l Gantt Chart