Software Cost and Schedule Estimation

1. Software Cost and Schedule Estimation Dr. Harry R. Erwin University of Sunderland <http://osiris.sunderland.ac.uk/~cs0her/> <mailto:harry.erwin@sunderland.ac.uk>

2. The Problems • Predicting software cost • Predicting software schedule • Controlling software risk

3. Criteria for a Good Model • Defined—clear what is estimated • Accurate • Objective—avoids subjective factors • Results understandable • Detailed • Stable—second order relationships • Right Scope • Easy to Use • Causal—future data not required • Parsimonious—everything present is important

4. Early Models • 1965 SDC Model • Putnam SLIM Model • Doty Model • RCA PRICE S Model • IBM-FSD Model • 1977 Boeing Model • 1979 GRC Model • Bailey-Basili Meta-Model • CoCoMo

5. 1965 SDC Model (Nelson 1966) • A linear regression of 104 attributes of 169 early software projects • Produces a MM estimate • Mean of 40 MM • Standard deviation of 62 MM • Counterintuitive—too much non-linearity in real program development

6. Putnam SLIM Model (Putnam 1978) • Commercially available • Popular with the US Government • Uses a Rayleigh distribution of project personnel level against time • DSI = C*(MM) (1/3) *(Schedule) (4/3) • Radical trade-off relationships

7. Doty Model (Herd et al., 1977) • Extended the SDC Model • MM = C(special factors)*(DSI) 1.047 • Problems with stability

8. RCA PRICE S Model (Freiman-Park, 1979) • Commercially available • Aerospace applications • Similar to CoCoMo (see below)

9. IBM-FSD Model (Walston-Felix, 1977) • Not fully described • Used by IBM to estimate programs • Some statistical concerns

10. 1977 Boeing Model (Black et al., 1977) • Similar to CoCoMo, but simpler • Out of use • Poor estimates

11. 1979 GRC Model (Carriere-Thibodeau, 1979) • Limited information available • Obvious typos and mistakes

12. Bailey-Basili Meta-Model (Bailey-Basili, 1981) • Rigorous statistical analysis of factors and size. • Not much experience

13. CoCoMo • Waterfall Model • Can be adapted to other models • Estimates: • Requirements analysis • Product design • Programming • Test planning • Verification and validation • Project office • CM and QA • Documentation

14. Where to Find CoCoMo • http://sunset.usc.edu/index.html • Or do a Google search on Barry Boehm.

15. Nature of Estimates • Man Months (or Person Months), defined as 152 man-hours of direct-charged labor • Schedule in months (requirements complete to acceptance) • Well-managed program

16. Input Data • Delivered source instructions (DSI) • Various scale factors: • Experience • Process maturity • Required reliability • Complexity • Developmental constraints

17. Basic Effort Model • MM = 2.4(KDSI)1.05 • More complex models reflecting the factors listed on the previous slide and phases of the program • The exponent of 1.05 reflects management overhead

18. Basic Schedule Model #include <iostream> #include <cmath> using namespace std; //introduces namespace std int main() { cout << "This is COCOMO Calc" << endl << endl; double old,newer,mm; for(;;) { cout << "Enter the manmonths estimated for the task. Enter 0 to quit" << endl; cin>>mm; if(mm<=0.0)break; cout<<endl<<"The following are 10/50/90 percentile estimates:"<<endl; old = pow(mm,0.32); newer = pow(mm,0.28); cout<<"Old COCOMO: "<<2.0*old<<'\t'<<2.5*old<<'\t'<<3.0*old<<endl; cout<<"New COCOMO: "<<0.8*3.67*newer<<'\t'<<3.67*newer<<'\t'<<1.2*3.67*newer<<endl; } return 0; }

19. Productivity Levels • Tends to be constant for a given programming shop developing a specific product. • ~100 SLOC/MM for life-critical code • ~320 SLOC/MM for US Government quality code • ~1000 SLOC/MM for commercial code

20. Nominal Project Profiles

21. What About Function Points? • Can also be used to estimate productivity. • Capers Jones (use Google to find) provides conversion factors between FPs and SLOC. <http://www.spr.com/> • The development organization needs previous experience with the problem domain to estimate FPs accurately. SLOC are easier to estimate with no experience.

22. More Sophisticated Modeling Incorporates: • Development Modes • Activity Distribution • Product Level Estimates • Component Level Estimates • Cost Drivers

23. Risk Analysis • A risk is a vulnerability that is actually likely to happen and will result in some significant effect • Standard software development risks: • Cost • Schedule (covaries with cost) • Technical (opposes cost) • Approach: • Identify them • Track them • Spend money to control them (Spiral Model)

24. Spiral Model • Defines early development activities to buy down risk • Maintains the interest of stakeholders • Takes longer and costs more • Ends with a standard Waterfall

25. Effects of Parallelism • Without parallelism, you do a critical path analysis. • With parallelism, statistical factors affect which task completes first. • With several parallel tasks of equal length, the mean schedule is about one standard deviation beyond that length. • Use Monte Carlo to study this.

26. Conclusions • Experience shows that seat-of-the-pants estimates of cost and schedule are only about 75% of the actuals. This amount of error is enough to get a manager fired in many companies. • Lack of hands-on experience is associated with massive cost overruns. • Technical risks are associated with massive cost overruns. • Do your estimates carefully! • Keep them up-to-date! • Manage to them!