SE423 · Chapter 6 · Annotated SlidesEstimation
Original
Overview

Estimation

Estimation methods, three-point estimating and COCOMO. Both finals had a COCOMO calculation (32 KLOC and 20 KLOC organic) and a “choose two estimation techniques” question, so the formulas on slides 39–47 and the method trade-offs on slides 9–16 are where marks are.

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Every original slide is shown unchanged. Yellow marks on a slide point to a numbered note: hover or tap a mark to jump to its note, and hover a note to see its marks. Questions tagged Past exam appeared on a real paper; Prediction questions are likely angles, not confirmed. Pick an answer to each question: a wrong pick tells you why it is wrong, so you can try again.

All 60 slides
  1. 1 Estimation
  2. 2 Outline
  3. 3 Estimation
  4. 4 Estimation
  5. 5 Estimation
  6. 6 Estimation: definitions
  7. 7 Cone of Uncertainty
  8. 8 Estimation Methodologies
  9. 9 Estimation Methodologies
  10. 10 Expert Judgment
  11. 11 Top-down Estimation
  12. 12 Bottom-up Estimation
  13. 13 Estimation by Analogy
  14. 14 Priced to Win
  15. 15 Algorithmic Measures
  16. 16 Wideband Delphi
  17. 17 Code-based Estimates
  18. 18 Function Points
  19. 19 Function Point Process
  20. 20 Effort Estimation
  21. 21 Duration
  22. 22 Duration
  23. 23 Causes of Variation
  24. 24 Estimation Issues
  25. 25 Over and Under Estimation
  26. 26 Introduction: Activity Resource Estimation
  27. 27 Controlling factors in resource estimation
  28. 28 Estimating tools
  29. 29 Activity Resource Estimation Output
  30. 30 Introduction: Activity Duration Estimation
  31. 31 Controlling factors in duration estimation
  32. 32 A useful rule of thumb
  33. 33 Estimating tools
  34. 34 Three Point Estimating
  35. 35 Example: Three Point Estimation of LOC
  36. 36 Example: Three Point Estimation using Function Points
  37. 37 COCOMO
  38. 38 COnstructive COst Model (COCOMO)
  39. 39 Basic COCOMO – More Constants
  40. 40 Basic COCOMO – The Modes
  41. 41 Basic COCOMO – Example
  42. 42 Basic COCOMO – Development Time
  43. 43 Basic COCOMO – Example
  44. 44 Basic COCOMO – Average Staff Size
  45. 45 Basic COCOMO – Productivity
  46. 46 COCOMO Complete Example - Organic
  47. 47 COCOMO Complete Example - Embedded
  48. 48 Intermediate COCOMO – Cost Drivers
  49. 49 Intermediate COCOMO – Cost Drivers
  50. 50 COCOMO Advantages
  51. 51 COCOMO Limitations
  52. 52 Final Notes
  53. 53 Code Reuse and Estimation
  54. 54 Estimation for Agile
  55. 55 Know Your Deadlines
  56. 56 Estimation Presentation (Communication)
  57. 57 Final Estimates
  58. 58 Estimation Guidelines
  59. 59 Other Estimation Factors
  60. 60 Other Estimation Notes
Slide 1 / 60

Estimation

Slide 1: Estimation

Notes

  1. Order of estimation: Size → Effort → Schedule. COCOMO turns size into effort and then into time.

Slide 2 / 60

Outline

Slide 2: Outline

Notes

  1. COCOMO (slides 37–51) is the calculation part; methods (9–17) are the written-answer part.

Slide 3 / 60

Estimation

Slide 3: Estimation
Slide 4 / 60

Estimation

Slide 4: Estimation

Notes

  1. You’re predicting work nobody has done yet, with people who may change, on requirements that may move. That’s why estimating is hard, and it’s never just a matter of being careful.

  2. Each step needs the one before. You can’t work out how much labor something takes without knowing how big it is, and you can’t set dates without knowing the labor. So the order is size → effort → schedule.

  3. Memory trick S.E.S.: Size, Effort, Schedule, always in that order.

Exam angle

PredictionQ1

What is the correct order of the basic estimation process?

Slide 5 / 60

Estimation

Slide 5: Estimation

Notes

  1. You can guess your trip home well because you’ve done it many times. Most software hasn’t been done before, so there’s no such “average”. That’s why even good estimates are often off by 25–100%.

Exam angle

PredictionQ1

Why are software estimates often off by so much?

Slide 6 / 60

Estimation: definitions

Slide 6: Estimation: definitions

Notes

  1. A target is what someone hopes for, and a commitment is what the team has actually checked it can do. Treating a wish as a promise is how impossible deadlines get born, so learn to say no.

  2. More work means more unknowns and more connections between them. So a small estimate may be quite close, while a very large one can be off by half. Uncertainty grows with size.

Exam angle

PredictionQ1

A target date is one the team commits to after building a schedule.

Slide 7 / 60

Cone of Uncertainty

Slide 7: Cone of Uncertainty

Notes

  1. Early estimates can be 0.25× to 4× the real value; the cone narrows as you learn.

  2. Seen again Ch 1.2, slide 40: Estimates change as the project evolves

Exam angle

PredictionQ1

At the feasibility stage, the cone of uncertainty says estimates may range from:

Slide 8 / 60

Estimation Methodologies

Slide 8: Estimation Methodologies
Slide 9 / 60

Estimation Methodologies

Slide 9: Estimation Methodologies

Notes

  1. Six methods. In an exam scenario, pick two and say WHY they fit (e.g., analogy early using past projects; bottom-up once a WBS exists).

  2. Memory trick “Every Team Brings A Pricing Algorithm”: Expert, Top-down, Bottom-up, Analogy, Priced-to-win, Algorithmic.

Tips & tricks

  • BEAT PoP: Bottom-up, Expert judgment, Analogy, Top-down, Priced to win, Parametric.

Exam angle

Past exam · Final 242

Q2.2 · two effort estimation techniques for the solar project

Past exam · Final 251

Q2.1 · estimation techniques and how to validate estimates

PredictionQ1

Choose two effort-estimation techniques for a 12-month hardware/software project and explain how each supports planning and risk control (Final 242).

Compare with the model answer

Bottom-up estimation: decompose the work into a WBS and estimate each task with the people who will do it; this is the most accurate, exposes hidden work and gives a baseline to track against. Three-point (PERT) estimating: for uncertain tasks like prototyping, get optimistic, most likely and pessimistic values and compute (O+4M+P)/6; the spread shows risk and sizes contingency reserves. Alternatives: analogy to past solar projects early on, Wideband Delphi for expert consensus, COCOMO for the software part. Validate estimates by comparing two methods and against historical data, and refine iteratively.

Slide 10 / 60

Expert Judgment

Slide 10: Expert Judgment

Notes

  1. Asking someone who’s done it before is fast and cheap. It’s only as good as their experience, though, so a guesstimate from someone whose last similar project was ten years ago may mislead you.

Exam angle

PredictionQ1

What does expert judgment’s accuracy depend on?

Slide 11 / 60

Top-down Estimation

Slide 11: Top-down Estimation

Notes

  1. You look at the whole project and judge it from similar size or type. It works early, when you don’t yet have the detail. The weakness is that hidden complexity in individual tasks gets missed, so it’s less accurate.

Exam angle

PredictionQ1

When is top-down estimation especially useful?

Slide 12 / 60

Bottom-up Estimation

Slide 12: Bottom-up Estimation

Notes

  1. You estimate each small task and add them up, which is why it needs a WBS. Detail improves accuracy, but it takes time, and the detail may not exist yet early on.

  2. Trap Top-down = fast but rough (early). Bottom-up = slow but accurate (needs WBS).

Exam angle

PredictionQ1

Which method requires a work breakdown structure and is the most granular?

Slide 13 / 60

Estimation by Analogy

Slide 13: Estimation by Analogy

Notes

  1. If last year’s similar project took six months, that’s real evidence, not a guess. The catch is that no two are the same, so you must adjust for the differences. That’s where analogy gets hard.

Exam angle

PredictionQ1

What is the main difficulty of estimating by analogy?

Slide 14 / 60

Priced to Win

Slide 14: Priced to Win

Notes

  1. Here the estimate starts from what will win the contract, not from the work. It can win the bid and still lead to overruns or cut corners. So priced to win isn’t really an estimate of effort at all.

Exam angle

PredictionQ1

A vendor underbids to win and plans to raise the cost through change requests. What’s this called?

PredictionQ2

What is the main risk of priced-to-win?

Slide 15 / 60

Algorithmic Measures

Slide 15: Algorithmic Measures

Notes

  1. These give you a number to feed into a formula. LOC and function points are the two to know, and note the slide’s honest point: most projects don’t use any of these.

Exam angle

PredictionQ1

Which are the most common algorithmic measures?

Slide 16 / 60

Wideband Delphi

Slide 16: Wideband Delphi

Notes

  1. Starting independently stops one loud person from anchoring everybody. Each round shares reasoning without pressure, so hidden factors come out, and the group’s numbers move closer. This is the idea behind Wideband Delphi.

  2. It’s useful when you have no past data, since it relies on people’s knowledge. But if everyone shares the same blind spot, they’ll happily agree on the wrong number.

  3. Seen again Ch 5, slide 22: Delphi technique for risk identification

Exam angle

PredictionQ1

Wideband Delphi requires historical project data.

Slide 17 / 60

Code-based Estimates

Slide 17: Code-based Estimates

Notes

  1. It’s easy to count finished code, but you can’t count code that doesn’t exist yet. And LOC rewards long code: 1,000 lines in one language may be 300 in another, so if people are paid by the line, you’ll get more lines.

Exam angle

PredictionQ1

Why is LOC hard to use for early estimates?

PredictionQ2

Developers are rewarded per line of code. What might happen?

Slide 18 / 60

Function Points

Slide 18: Function Points

Notes

  1. This measures what the software does for users, rather than how it’s written. So it can be estimated earlier and compared across languages, which is the main advantage of function points over LOC.

Exam angle

PredictionQ1

How do function points measure size?

Slide 19 / 60

Function Point Process

Slide 19: Function Point Process

Notes

  1. First you count what the system does, then weight each thing for how hard it is. Then you adjust for the overall situation, such as performance demands. Order matters: count → weight → adjust.

Exam angle

PredictionQ1

How many influence factors adjust the function point count?

Slide 20 / 60

Effort Estimation

Slide 20: Effort Estimation

Notes

  1. Size tells you how big it is, and effort tells you how much work that means. A skilled, motivated team needs less effort for the same size, which is why people factors are part of this conversion.

Exam angle

PredictionQ1

Which model estimates effort from past project data?

Slide 21 / 60

Duration

Slide 21: Duration

Notes

  1. Effort is how much work there is, and duration is how long it takes on the calendar. Five people working one day is 5 person-days of effort but only one day of duration.

  2. Trap Effort ≠ duration. Adding people can shorten duration but not effort.

Exam angle

PredictionQ1

Five people review a document for 10 hours. Effort and duration are:

Slide 22 / 60

Duration

Slide 22: Duration

Notes

  1. Nobody works on a task for all eight hours of a day, with meetings and interruptions. So 40 hours of labor doesn’t mean one week, and treating labor time as clock time makes every schedule too short.

Exam angle

PredictionQ1

A task needs 40 hours of labor, and productivity is about 75%. How long will it take on the clock?

Slide 23 / 60

Causes of Variation

Slide 23: Causes of Variation

Notes

  1. An estimate assumes an average person on a normal day, and real days aren’t like that. Each cause is a way reality differs from that assumption, so variation is normal, not a failure.

Tips & tricks

  • The causes, in slide order: Very Unlucky Estimators Miss. Varying skill levels, Unexpected events, Efficiency of work time, Mistakes and misunderstandings.

Exam angle

PredictionQ1

Which is a cause of variation in duration?

Slide 24 / 60

Estimation Issues

Slide 24: Estimation Issues

Notes

  1. You need accurate numbers at the start, and you only get them at the end. Recording actual results helps because they make your next estimate better. That’s why past experience is the best basis.

Exam angle

PredictionQ1

Precise estimation data only arrives at the end. Why record it anyway?

Slide 25 / 60

Over and Under Estimation

Slide 25: Over and Under Estimation

Notes

  1. Both directions hurt. Too high and it won’t get funded, and any spare time gets filled anyway (Parkinson’s law). Too low and people cut corners, usually testing, and morale falls when dates keep slipping.

Exam angle

PredictionQ1

“Work expands to fill the time available” is:

Slide 26 / 60

Introduction: Activity Resource Estimation

Slide 26: Introduction: Activity Resource Estimation

Notes

  1. You need more than a headcount. A task may require one specific expert in March, not “a developer” sometime, so asking who, how much and when catches clashes before they happen.

Exam angle

Past exam · Final 242

Related question: Q2.3 · allocating resources across tasks

Past exam · Final 251

Related question: Q2.2 · resourcing the project

Slide 27 / 60

Controlling factors in resource estimation

Slide 27: Controlling factors in resource estimation

Notes

  1. You may want three experts, but you can only plan with what’s really available. These three things limit your options, so a good estimate reflects availability, not just what the task ideally needs.

Tips & tricks

  • ORE: Organization policy, Resource availability, Enterprise environment.

Exam angle

PredictionQ1

Which is a controlling factor in resource estimation?

Slide 28 / 60

Estimating tools

Slide 28: Estimating tools

Notes

  1. Different tools answer different questions. Alternatives analysis asks whether to build, buy or hire, and each choice leads to a different resource estimate.

Exam angle

PredictionQ1

A team compares in-house development, contracting and COTS. Which tool is this?

Slide 29 / 60

Activity Resource Estimation Output

Slide 29: Activity Resource Estimation Output

Notes

  1. These add up from activity to work package, to give a total. The resource calendar matters because a person on holiday in week 3 can’t do week 3’s work, even though the numbers add up.

Exam angle

PredictionQ1

What does a resource calendar document?

Slide 30 / 60

Introduction: Activity Duration Estimation

Slide 30: Introduction: Activity Duration Estimation

Notes

  1. Those closest to the work know how long it really takes, so ask them. As you learn more, estimates improve, which is why they’re progressively elaborated, rather than fixed at the start.

Exam angle

PredictionQ1

Who should provide duration estimates?

Slide 31 / 60

Controlling factors in duration estimation

Slide 31: Controlling factors in duration estimation

Notes

  1. More people can lower productivity (Mythical Man-Month, Chapter 6.2).

  2. Seen again Ch 6.2, slide 56: Mythical man-month

Exam angle

PredictionQ1

Why might adding people not shorten a task proportionally?

Slide 32 / 60

A useful rule of thumb

Slide 32: A useful rule of thumb

Notes

  1. Mixing hours and days causes silly mistakes, like adding 2 days and 3 hours as 5. One unit throughout avoids this, and you convert only for the final presentation.

Exam angle

PredictionQ1

What’s the rule of thumb for time units?

Slide 33 / 60

Estimating tools

Slide 33: Estimating tools

Notes

  1. A buffer is time you added on purpose because something may go wrong. Slack is spare time that just happens to exist on a task not on the critical path. So one is planned protection, and the other results from the network.

  2. One single number hides how uncertain you are. Taking three and averaging builds in that range. This simple version treats all three equally.

  3. Seen again Ch 6.2, slide 34: Slack time

Exam angle

PredictionQ1

What’s the difference between buffer time and slack?

Slide 34 / 60

Three Point Estimating

Slide 34: Three Point Estimating

Notes

  1. PERT/beta formula: (O + 4M + P) / 6 weights the most likely value 4×.

  2. Memory trick One, four, one, over six.

Tips & tricks

  • MOP: Most likely, Optimistic, Pessimistic.

Exam angle

PredictionQ1

O = 4 days, M = 6 days, P = 14 days. PERT estimate?

PredictionQ2

O = 2, M = 5, P = 8. Basic (simple average) three-point estimate?

Slide 35 / 60

Example: Three Point Estimation of LOC

Slide 35: Example: Three Point Estimation of LOC

Notes

  1. It’s just the formula applied to each function, then added up. Weighting the most likely value by 4 means one extreme guess can’t pull the answer too far. Check one row yourself to practice.

Exam angle

PredictionQ1

Optimistic 1,500, most likely 2,300 and pessimistic 3,100 LOC. What’s the estimate?

Slide 36 / 60

Example: Three Point Estimation using Function Points

Slide 36: Example: Three Point Estimation using Function Points

Notes

  1. Weights (low/average/high): inputs 3/4/6, outputs 4/5/7, queries 3/4/6, internal files 7/10/15, external interfaces 5/7/10.

Exam angle

PredictionQ1

Which function category has the highest weights in the table?

Slide 37 / 60

COCOMO

Slide 37: COCOMO
Slide 38 / 60

COnstructive COst Model (COCOMO)

Slide 38: COnstructive COst Model (COCOMO)

Notes

  1. It’s a formula built from data on many past projects, and that’s its strength. You give it a size and it gives you effort, so it’s only as reliable as that size estimate.

  2. Here’s the catch: you need to know the size early, which is exactly when you know least. So a confident-looking result can rest on a weak guess.

Exam angle

PredictionQ1

The biggest weakness of COCOMO is that it:

Slide 39 / 60

Basic COCOMO – More Constants

Slide 39: Basic COCOMO – More Constants

Notes

  1. The exponent b is above 1, which is the important part: doubling the code more than doubles the effort, as bigger projects carry more coordination.

  2. Don’t mix these up in an exam: a, b give effort, and c, d give time.

Exam angle

Past exam · Final 242

Q2.4 · COCOMO organic, 32 KLOC: effort, time, staff

Past exam · Final 251

Q2.5 · COCOMO organic, 20 KLOC: effort, time, productivity, staff

Slide 40 / 60

Basic COCOMO – The Modes

Slide 40: Basic COCOMO – The Modes

Notes

  1. Remember size isn’t the only thing that decides. A small project with severe constraints may still be embedded, because it’s the difficulty that drives effort.

  2. Note c = 2.5 for all three modes.

  3. Memory trick a goes 2.4 → 3.0 → 3.6 (+0.6 each). d goes 0.38 → 0.35 → 0.32 (−0.03 each). c is always 2.5.

Tips & tricks

  • hOSE, from small to large: Organic, Semi-detached, Embedded.

Exam angle

PredictionQ1

Which COCOMO mode has a = 3.0 and b = 1.12?

PredictionQ2

In Basic COCOMO, c = 2.5 for all three modes.

Slide 41 / 60

Basic COCOMO – Example

Slide 41: Basic COCOMO – Example

Notes

  1. Same amount of code and more than three times the effort. That’s what difficulty does, so picking the right mode matters as much as the size.

Exam angle

PredictionQ1

For the same 200 KLOC, which mode needs the most effort?

Slide 42 / 60

Basic COCOMO – Development Time

Slide 42: Basic COCOMO – Development Time

Notes

  1. Time TDEV = c × E^d (months). Uses E, not KLOC.

  2. Trap TDEV uses EFFORT (E) as input, not KLOC.

Exam angle

PredictionQ1

What’s the input to the TDEV formula?

Slide 43 / 60

Basic COCOMO – Example

Slide 43: Basic COCOMO – Example

Notes

  1. Interesting: all three give ~29 months. Harder projects need more PEOPLE, not more time.

Exam angle

PredictionQ1

The three modes all give about 29 months. What does this suggest?

Slide 44 / 60

Basic COCOMO – Average Staff Size

Slide 44: Basic COCOMO – Average Staff Size

Notes

  1. Total work divided by time gives how many people you need on average. It’s a check on your plan: if it says 23 people and you have 5, the schedule isn’t realistic.

Exam angle

PredictionQ1

E = 20 staff-months and TDEV = 8 months. How many staff on average?

Slide 45 / 60

Basic COCOMO – Productivity

Slide 45: Basic COCOMO – Productivity

Notes

  1. This tells you how much output to expect per person. Use it to sanity-check, since if the required rate is far above your team’s normal rate, something in the estimate is wrong.

Exam angle

PredictionQ1

How is productivity calculated?

Slide 46 / 60

COCOMO Complete Example - Organic

Slide 46: COCOMO Complete Example - Organic

Notes

  1. The full recipe: E → TDEV → SS = E/TDEV → P = LOC/E. Exactly what both finals asked.

  2. Tip Show every step with the formula, keep 1 decimal, and give units (person-months, months, people, LOC/PM). Partial marks are per step.

Exam angle

Past exam · Final 242

Q2.4 · COCOMO organic, 32 KLOC: effort, time, staff

Past exam · Final 251

Q2.5 · COCOMO organic, 20 KLOC: effort, time, productivity, staff

PredictionQ1

COCOMO organic, 32 KLOC (a=2.4, b=1.05, c=2.5, d=0.38). Compute effort, development time and average staff (Final 242).

Compare with the model answer

E = 2.4 × 32^1.05 ≈ 2.4 × 38.06 ≈ 91.3 person-months. TDEV = 2.5 × 91.3^0.38 ≈ 2.5 × 5.56 ≈ 13.9 months. Average staff = E / TDEV ≈ 91.3 / 13.9 ≈ 6.6 people (about 7).

PredictionQ2

COCOMO organic, 20 KLOC. Compute effort, development time, productivity and staff (Final 251).

Compare with the model answer

E = 2.4 × 20^1.05 ≈ 2.4 × 23.2 ≈ 55.8 person-months. TDEV = 2.5 × 55.8^0.38 ≈ 2.5 × 4.61 ≈ 11.5 months. Productivity = 20,000 LOC / 55.8 PM ≈ 359 LOC per person-month. Staff = 55.8 / 11.5 ≈ 4.8 people (about 5).

Slide 47 / 60

COCOMO Complete Example - Embedded

Slide 47: COCOMO Complete Example - Embedded

Notes

  1. Answer to “Conclusion ?”: an embedded project needs far more effort per line, so expected productivity is only ~127 LOC per person-month (vs 375 organic).

Exam angle

PredictionQ1

An organic project is estimated at E = 40 PM and TDEV = 10 months. Average staff?

Slide 48 / 60

Intermediate COCOMO – Cost Drivers

Slide 48: Intermediate COCOMO – Cost Drivers

Notes

  1. Basic COCOMO only looks at size, yet a strong team on a familiar platform needs less effort. Cost drivers add these real-world factors, making the estimate more realistic.

Exam angle

PredictionQ1

Why add cost drivers to COCOMO?

Slide 49 / 60

Intermediate COCOMO – Cost Drivers

Slide 49: Intermediate COCOMO – Cost Drivers

Notes

  1. Each driver is a multiplier: above 1 adds effort, below 1 reduces it. Multiply them together to get C, then scale the basic effort by it.

Exam angle

PredictionQ1

In Intermediate COCOMO the cost-driver multipliers are added together.

Slide 50 / 60

COCOMO Advantages

Slide 50: COCOMO Advantages

Notes

  1. Being based on real projects means it isn’t just opinion. Anyone using it with the same inputs gets the same answer, which makes it easy to explain and defend. That’s why repeatable counts for so much.

Exam angle

PredictionQ1

Which is an advantage of COCOMO?

Slide 51 / 60

COCOMO Limitations

Slide 51: COCOMO Limitations

Notes

  1. Each item is something that affects real effort but isn’t in the formula. So treat its result as a starting point. And you must predict size first, which is hard early on.

Exam angle

PredictionQ1

Which is a COCOMO limitation?

Slide 52 / 60

Final Notes

Slide 52: Final Notes
Slide 53 / 60

Code Reuse and Estimation

Slide 53: Code Reuse and Estimation

Notes

  1. Reuse looks free, but someone must still understand, adapt and test that code. Rewriting most of something is often just as hard as new code, which explains the 50% rule.

Tips & tricks

  • NoRM: New, Reused, Modified.

Exam angle

PredictionQ1

Code is 60% modified. How should it be estimated?

PredictionQ2

About how much effort does reused code take, compared with new?

Slide 54 / 60

Estimation for Agile

Slide 54: Estimation for Agile

Notes

  1. Agile doesn’t skip estimating. It estimates smaller things, closer to when they’re built, so each one is easier. Adding up tasks gives a total for the increment that’s easier to trust.

Exam angle

PredictionQ1

How does agile estimation get a total?

Slide 55 / 60

Know Your Deadlines

Slide 55: Know Your Deadlines

Notes

  1. Before fighting for a date, ask why it exists. A legal deadline can’t move, so you’d cut scope, while a date someone just picked may be negotiable. That’s the difference between real and artificial.

Exam angle

PredictionQ1

A contract requires delivery by year-end. What kind of deadline is this?

Slide 56 / 60

Estimation Presentation (Communication)

Slide 56: Estimation Presentation (Communication)

Notes

  1. “Six months” sounds like a promise. A range or a confidence level says what you really know, and it leads to better decisions. That’s why how you present it has so much impact.

Exam angle

PredictionQ1

“July 1 with 50% probability” is which presentation technique?

Slide 57 / 60

Final Estimates

Slide 57: Final Estimates

Notes

  1. Known risks get money the PM can use right away, and unforeseen ones need permission. That separation keeps both visible, rather than hiding extra padding in each task.

  2. Seen again Ch 1.2, slide 47: Contingency vs. management reserves

Tips & tricks

  • ARM, already in order: Aggregation, Risk analysis, Management reserves.

Exam angle

PredictionQ1

Who controls contingency reserves in final estimates?

PredictionQ2

How do you access management reserves?

Slide 58 / 60

Estimation Guidelines

Slide 58: Estimation Guidelines

Notes

  1. Early estimates are rough and later ones are better, so keep updating them. But changing plans too often is chaotic, which is why the slide asks you to balance. Iterative doesn’t mean constant.

Exam angle

PredictionQ1

What does “estimate iteratively” mean?

Slide 59 / 60

Other Estimation Factors

Slide 59: Other Estimation Factors

Notes

  1. Plans often assume every working hour goes to the project. Real days include meetings and sick leave, and a new person needs extra help. Ignoring non-project time makes each estimate optimistic.

Exam angle

PredictionQ1

Why allow for non-project time?

Slide 60 / 60

Other Estimation Notes

Slide 60: Other Estimation Notes

Notes

  1. People tend to start late when they believe there’s plenty of time. So added padding gets wasted, not used as protection. Student syndrome is why extra time doesn’t always mean you’ll finish safely.

Exam angle

PredictionQ1

Team members who wait until the last minute to start a task despite padding show: