Software Estimation: What, Why & How

1. Software Estimation: What, Why & How Nupul Kukreja 17th September 2013

3. Based OnSoftware Estimation: Demystifying The Black Art Steve McConnell Microsoft Press.

4. Agenda • Purpose of Estimation • Estimation and Plans • What are “Good Estimates”? • Estimation Quiz • Under-estimation or Over-estimation? • Estimation Tools and Techniques

5. Why Do We Estimate? • To determine if project’s targets are realistic enough to control progress to meet them • And NOT to predict a project’s outcome • Estimate vs. Target Gap: • ≤ 20%  Easy to control feature set, schedule, team size towards realization • Else  Not possible to control project towards successful realization  Targets need to be better aligned with reality

6. Estimates vs. Plans • Estimates form foundations for plans • Plans don’t have to be same as estimates • If (Targets – Estimates) >> 1 • Plans must account for high risk • Else • Plans can assume less risk • Accurate Estimates needed for: • Creating a detailed schedule • Prioritizing functionality for delivery • Breaking project into iterations etc.

7. Estimates As Probability Statements • Single-point estimates “assume” 100% odds of success; NOT realistic • Usually a ‘Target’ in disguise • Must factor in uncertainty  i.e. project success follows a probability distribution 100% Nominal Outcome Nominal Outcome Probability Schedule (or Cost of Effort) Schedule (or Cost of Effort) Common Assumption More Realistic

8. What Is A Good Estimate? “An estimate that provides a clear enough view of the project reality to allow the project leadership to make good decision about how to control the project to hit its targets” As quoted from Steve McConnell’s book.

9. How Good an Estimator Are YOU? • Estimate the questions (in the following slide) to the best of your ability (WAGs) • Fill lower/upper bound so that there is a 90% chance of including the correct value • Please estimate without any electronic, human or supernatural help 

10. Estimation Quiz

11. Love Vegas? • You win $1000 if the actual answer is within your range OR • You win $1000 by spinning the wheel below: $0 10% 90% Win $1000

12. Estimation Quiz 10% 90%

13. Implications of Choice Desirable: Set range just right so as to be indifferent between gamble and your estimate i.e. 90% chance, not more and not less, that answer is within range 90% Confident  90% of the time 90% of the answers within range (i.e. you get 9 answers correct on 9 of 10 such quizzes)

14. Accuracy of Estimates Effort, Cost, Schedule Nonlinear penalty due to planning errors, upstream defects, high-risk practices Linear penalty due to Parkinson’s Law or Student’s Syndrome ←Underestimation Overestimation→ < 100% 100% > 100% Target as a Percentage of Nominal Estimate Penalties of underestimation more severe than those for overestimation. If you can’t estimate with complete accuracy, it’s better to err on the side of overestimation – Steve McConnell

15. Estimation Techniques

16. Count, Compute, Judge • Count First – If you can count something directly please do so • If you can’t count the answer directly, count something else (i.e. correlated to the item you wish to estimate) and compute the answer (preferably by using calibration data) • Use judgment as a last resort

17. Fermi-lize Your Estimation Skills • Enrico Fermi – Won a Nobel Prize in Physics in 1938 • Well known for his creative and intuitive, even casual sounding estimates • A "Fermi question" is a question which seeks a fast, rough estimate of quantity which is either difficult or impossible to measure directly. Ex: • Number or chauffeurs currently in LA • Number of piano repairmen in Chicago

18. 1. Fermi Decomposition • Figure out something that is known about the quantity in question • Estimate other things that may have a bearing on that quantity – it’s okay to have rough approximations • Sometimes you can just Google some numbers to extrapolate from there • It’s ALWAYS possible to estimate and get ballpark idea about the quantity in question

19. Team In-Class • How many golf balls can fit in this class room (OHE 123)?* *DEN Students: You may think of the room you are viewing the lecture in 

20. 2. Individual Expert Judgment • Most common estimation approach • Experts those are doing the task • Task level estimation: • Decompose estimates into tasks requiring no more than 2 days of effort (rule of thumb to avoid estimation error) • Example: Estimate duration for: • As a user I can log in to the system, via valid a username and password, so that I can access my account data

21. Individual Expert Judgment Example of developer single-point estimates (not preferable) Example of individual estimation using best case and worst case. Provides better estimates Forces thinking of worst case estimates – leading to better overall range

22. Individual Expert Judgment Even Better: Compute a 3-point estimate including the most-likely case Compute expected case using the PERT formula: Expected Case = [BestCase + (4* MostLikelyCase) + WorstCase]/6

23. Individual Expert Judgment Compare estimates to actuals to improve estimation accuracy over time Magnitude of Relative Error: |(Actual – Estimate)/Actual|

24. 3. Decomposition & Recomposition • Idea: • Separate an estimate into multiple pieces • Estimate each piece individually • Recombine individual estimates into an overall aggregate estimate • AKA “bottom up” estimation or “Work Breakdown Structure (WBS) • Very important and highly used technique • Leads to quite accurate estimates

25. Work Breakdown Structure (WBS) • Example: Cost of owing and operating a car • Buy the car • Pay down payment • Pay taxes, licensing and registration fees • Insure the car • Pay monthly loan installments • Operate and maintain the car • Pay semi-annual insurance payments • Fill car with gas when needed • Change oil every 3000 miles • Take car to oil change shop • Let them do work • Pay fees and taxes • Drive back • Other routine maintenance • Sell the car Estimate each piece individually and aggregate the estimates all the way to the top Law of Large Numbers: Overestimating some pieces will help cancel out some of the underestimates of the rest. Leading to better estimates

27. 4. Estimation by Analogy • Create estimates of new project by comparing it to a similar past project • Can help create accurate estimates (by following the process below, instead of relying on memory) • Get detailed size, effort and cost results for similar previous project (WBS is preferable if possible) • Compare size of new, piece-by-piece to previous • Build up estimate for new project’s size as a percentage of old project’s size • Create an effort estimate based on size of new project compared to that of previous one • Check for consistent assumptions across the two

28. 5. Proxy-Based Estimates • Very difficult to estimate SLOC count looking at a feature • Or expected #defects, #test-cases, #classes etc., • Proxy-based estimation: • Identify a proxy that is correlated with quantity to be estimated • Proxy is usually easier to estimate/count or available sooner in project • Compute estimate based on proxy and past historical data • Useful for creating whole-project or whole-iteration estimates but NOT for detailed task-by-task or feature-by-feature • Example of two proxy-based estimates…

29. 5.1 Story Points • Unit-less measure of ‘complexity’ or ‘size’ of feature • Scales: • Powers of 2: 1, 2, 4, 8, 16 … • Fibonacci: 1, 2, 3, 5, 8, 13 … • #Story-Points per iteration = Velocity i.e. looking at past velocity estimate completion time of project (use historical data if available or forecast or run one iteration)

30. Story Point Practice As a user I can log in to the system, via valid a username and password, so that I can access my account data 1 3 5 8 13 20 40 100

31. 6. Expert Judgments in Groups • Similar to planning poker • Have each team member estimate pieces of project individually • Meet to compare the estimates • Reach mutual consensus as group • Without averaging the estimates. You may average the estimates but you still need to discuss individual results • Extremely effective technique to help improve estimation accuracy

32. 7. Estimation by Tools • Helps perform tasks that can’t be done manually • Simulating project outcomes (i.e. sensitivity analysis) • Probability Analysis i.e. viewing the (cumulative) probability distribution of the estimates • What-if analyses • Serves as referee for unrealistic project expectations • Estimation of less common software issues • Works best if you have historical data for calibration

33. List of Available Tools • COCOMO II • Construx Estimate • Costar (commercial implementation of COCOMO II) • Price-S • SEER • SLIM-Estimate and Estimate Express • Home grown?

34. Using Multiple Approaches • No single technique is perfect • Best to augment estimation with multiple approaches • Each approach works best in specific context • Convergence good estimate • Divergence  something overlooked or needs to be understood better

35. Conclusion • Estimation is NOT an art (somewhat but not entirely) • Can effectively be executed as a science • Need not rely purely on intuition or memory • Improves over time especially if historical data is captured • Requires basic arithmetic to understand • Complex models can be created with the help of statistics – premise of most ‘tools’ • VERYYYYYYYYYYYYYYYY Critical skill-set to have in the 21st Century 

36. References • Software Estimation: Demystifying the black art – Steve McConnell • How to Measure Anything – Doug Hubbard • Software Estimation Economics – Barry Boehm (a.k.a., The Blue Book) • Software Cost Estimation with COCOMO – Boehm et. al.