1. CSC444F'05Lecture 51 The Stochastic Capacity Constraint

2. CSC444F'05Lecture 52 MIDTERM NEW DATE AND TIME AND PLACE Tuesday, November 1 8pm to 9pm Woodsworth College WW111

3. CSC444F'05Lecture 53 Estimates Estimates are never 100% certain E.g, if we estimate a feature at 20 ECD’s –Not saying will be done in 20 ECDs –But then what are we saying? Are we confident in it? Is it optimistic? Is it pessimistic? A quantity whose value depends upon unknowns (or upon random chance) is called a stochastic variable Release planning contains many such stochastic variables.

4. CSC444F'05Lecture 54 Confidence Intervals Say we toss a fair coin 5000 times –We expect it to come up heads ½ the time – 2500 times or so –Exactly 2500? Chance is only 1.1% –≤ 2500? Chance is 50% If we repeat this experiment over and over again (tossing a coin 5000 times), on average ½ the time it will be more, ½ the time less. –≤ 2530? Chance is 80% –≤ 2550? Chance is 92% These (50%, 80%, 92%) are called confidence intervals –With 80% confidence we can say that the number of heads will be less than 2530.

5. CSC444F'05Lecture 55 Stochastic Variables Consider the work factor of a coder, w. –When estimating in advance, w is a stochastic variable. –Stochastic variables are described by statistical distributions –A statistical distribution will tell you: For any range of w The probability of w being within that range –Can be described completely with a probability density function. X-axis: all possible values of the stochastic variable Y-axis: numbers >= 0 The probability that the stochastic variables lies between two values a and b is given by the area under the p.d.f. between a and b.

6. CSC444F'05Lecture 56 PDF for w Probability that 0.5 < w < 0.7 = 66% Looks to be fairly accurate. –Has a finite probability of being 0 –Has not much chance of being much greater than 1.2 or so Drawing such a curve is the only real way of describing a stochastic variable mathematically.

7. CSC444F'05Lecture 57 Parameterized Distributions “So, Bill, here’s a piece of paper, could you please draw me a p.d.f. for your work factor?” –Nobody knows the distribution to this level of accuracy –Very hard to work with mathematically Usual method is to make an assumption about the overall shape of the curve, choosing from a few set shapes that are easy to work with mathematically. Then ask Bill for a few parameters that we can use to fit the curve. Because we are not so sure on our estimates anyways, the relative inaccuracy of choosing from one of a set of mathematically tractable p.d.f.’s is small compared to the other estimation errors.

8. CSC444F'05Lecture 58 e.g., a Normal for w Assume work factors are adequately described by a bell-shaped Normal distribution. 2 points are required to fit a Normal E.g., average case and some reasonable “worst case”. –Average case: half the time less, half the time more = 0.6 –“Worst” case: 95% of the time w won’t be that bad (small) = 0.4 Normal curves that fits is N(0.6,0.12). area = 68%

9. CSC444F'05Lecture 59 Maybe not Normal Normals are easiest to work with mathematically. May not be the best thing to use for w –Normal is symmetric about the mean E.g., N(0.6,0.12) predicts a 5% “best case” of 0.8. What if Bill tells us the 5% best case is really 1.0? –Then can’t use a Normal –Would need a skewed (tilted) distribution with unsymmetrical 5% and 95% cases. –Normal extends to infinity in both directions Finite probability of w 10

10. CSC444F'05Lecture 510 Estimates Most define our quantities very precisely E.g., for a feature estimate of 1 week –Post-Facto What are the units? 40 hours? Longer? Shorter? Dedicated? Disrupted? One person or two?... Dealt with this last lecture in great detail –Stochastic 1 week best case? 1 week worst case? 1 week average case? Need a p.d.f Depending upon these concerns, my “1 week” maybe somebody else’s 4 weeks. –Very significant issue in practice

11. CSC444F'05Lecture 511 The Stochastic Capacity Constraint T is fixed F and N are both stochastic quantities. Can only speak about the chance of the goo fitting into the rectangle Say F=400, N=10, T=40: are we good to go? –Cannot say. –Need precise distributions to F and N to answer, and then only at some confidence level.

12. CSC444F'05Lecture 512 Summing Distributions F and N are sums and products over many contributing stochastic variables. E.g. –F = f1 + f2 –If f1 and f2 have associated statistical distributions, what is the statistical distribution of F? –In general, no answer. –Special case: f1 and f2 are both Normal Then F will be Normal as well. Mean of F will be the sums of the means of f1 and f2 Standard deviation of F will be the square root of the sums of the squares of the standard deviations of f1 and f2. –How about f1 * f2? Figet about it! Huge formula, result is not a Normal distribution –One needs statistical simulation software tools to do arithmetic on stochastic variables.

13. CSC444F'05Lecture 513 Law of Large Numbers If we sum lots and lots of stochastic variables, the sum will approach a Normal distribution. Therefore something like F is going to be pretty close to Normal. –E.g., 400 features summed N will also be, but a bit less so –E.g., 10 w’s summed

14. CSC444F'05Lecture 514 Delta Statistic D(T) = N  T  F If we have Normal approximations for N and F, can compute the Normal curve for D as a function of various T’s. We can then choose a T that leads to a D we can live with. Interested in Probability [ D(T)  0 ] The probability that all features will be finished by dcut. In choosing T will want to choose a confidence interval the company can live with, e.g., 80%. Then will pick a T such that D(T)  0 80% of the time.

15. CSC444F'05Lecture 515 Example Picking T F is Normal with mean 400 and 90% worst case 500 N is Normal with mean 10 and 90% worst case 8 Cells are D(T) = N  T  F at the indicated confidence level Note transitions through 0. confidence level 25%40%50%60%80%90%95% 30-39-77-100-123-177-217-250 3514-26-50-74-130-172-207 4067250-25-84-128-164 T 45121775023-38-85-123 50174128100727-41-82 55228179150121521-41 6028223120016997440

16. CSC444F'05Lecture 516 Choices for T To be 95% certain of hitting the dates, choose T = 60 workdays Or... If we plan to take 40 workdays, only 5% of the time will be late by more than 20 workdays To be 80% sure, T = 49 To gamble, for a 25% fighting chance, make T = 33.

17. CSC444F'05Lecture 517 Shortcut Ask for 80% worst case estimates for everything. If F = NxT using the 80% worst case values, then there is an 80% chance of making the release. The Deterministic Release Plan is based on this approach. If you also ask for mean cases for everything, can then fit a Normal distribution for D(T) and can predict the approximate probability of slipping.

18. CSC444F'05Lecture 518 Initial Planning Start with a T Choose a feature set See if the plan works out If not, adjust T and/or the feature set an continue

19. CSC444F'05Lecture 519 Adjusting the Release Plan Count on the w estimated to be too high and feature estimates to be too low. Re-adjust as new data comes in. Can “pad the plan” by choosing a 95% T. –Will make it with a high degree of confidence –May run out of work –May gold plate features Better to have an A-list and a B-list –Choose one T such that, e.g., Have 95% confidence of making the A list Have 40% confidence of making the A+B list.

20. CSC444F'05Lecture 520 Appreciating Uncertainty Successful Gamblers and Traders –Really understand probabilities Both will tell you the trick is to know when to take your losses In release planning, the equivalent is knowing when to go to the boss and say –We need to move out the date –Or we need to drop features from the plan

21. CSC444F'05Lecture 521 Risk Tolerance Say a plan is at 60% Developer may say: –Chances are poor: 60% at best An entrepreneurial CEO will say –Looking great! At least a 60% chance of making it. Should have an explicit discussion of risk tolerance

22. CSC444F'05Lecture 522 Loading the Dice Can manage to affect the outcome. Like a football game: –Odds may be 3-to-1 against a team winning –But by making a special effort, the team may still win In release planning –Base the odds on history –But as a manager, don’t ever accept that history is as good as you can do! E.g., introduce a new practice that will boost productivity –Estimate will increase productivity by 20% –Don’t plan for that! –Plan for what was achieved historically. –Manage to get that 20% and change history for next time around.

23. CSC444F'05Lecture 523 Example Stochastic Release Plan Sample Stochastic Release PlanStochastic Release Plan