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Effort and Schedule Estimation Copyright, 1999 © Jerzy R. Nawrocki Personal Software Process Lecture 7

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J. Nawrocki, PSP, Lecture 7 Introduction Time & defect recording Time & defect recording Coding strd+Size measuremnt+PIP Coding strd+Size measuremnt+PIP Size estimating + Test report Task & schedule planning Code & design reviews Code & design reviews Design templates Design templates Cyclic dev. Cyclic dev. Baseline Planning Quality Cyclic

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J. Nawrocki, PSP, Lecture 7 Introduction begin.. end 500 LOC

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J. Nawrocki, PSP, Lecture 7 Plan of the lecture IntroductionIntroduction From the previous lectureFrom the previous lecture Effort estimationEffort estimation Multiple estimatesMultiple estimates Schedule estimatingSchedule estimating Progress trackingProgress tracking

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J. Nawrocki, PSP, Lecture 7 From the previous lecture.. Humphrey, CMU, 1995 PROxy-Based Estimating Objects as proxies StandardcomponentmethodFuzzylogicmethod Probemethod

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J. Nawrocki, PSP, Lecture 7 From the previous lecture.. 4. Knowing: programming language object type size ranges the number of methods estimate, using historical data, size of each object.

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J. Nawrocki, PSP, Lecture 7 From the previous lecture.. 6. Apply linear regression to get estimated program size Y: Y = 1 X means 10 5 means 10

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J. Nawrocki, PSP, Lecture 7 From the previous lecture.. 7. Using the t distribution and standard deviation compute the prediction interval for a given percentage. For 100% the For 100% the interval is [0; + ]

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J. Nawrocki, PSP, Lecture 7 From the previous lecture.. (X - x avg ) 2 (x i - x avg ) 2 (x i - x avg ) n +1 Range = t Range = t 7c. Compute the range as follows: Initial estimate obtained in Step 5

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J. Nawrocki, PSP, Lecture 7 Plan of the lecture IntroductionIntroduction From the previous lectureFrom the previous lecture Effort estimationEffort estimation Multiple estimatesMultiple estimates Schedule estimatingSchedule estimating Progress trackingProgress tracking

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J. Nawrocki, PSP, Lecture 7 Effort estimation begin.. end Programs written so far Historical data It should take... man month to finish the project

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J. Nawrocki, PSP, Lecture 7 Effort estimation begin.. end Estimatedsize Actualtime Historical data

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J. Nawrocki, PSP, Lecture 7 Effort estimation begin.. end Estimatedsize Actualtime Historical data r 2 0.5

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J. Nawrocki, PSP, Lecture 7 Effort estimation Estimated size Actual time 1. 0, 1 2. Effort = 1 * Estimated_size n Range = t 3. Range = t r Effort min = Effort - Range

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J. Nawrocki, PSP, Lecture 7 Effort estimation begin.. end Estimatedsize Actualtime Historical data Lack of data or lack of correlation between estimated size and actual time

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J. Nawrocki, PSP, Lecture 7 Effort estimation begin.. end Actualsize Actualtime Historical data

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J. Nawrocki, PSP, Lecture 7 Effort estimation begin.. end Actualsize Actualtime Historical data r 2 0.5

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J. Nawrocki, PSP, Lecture 7 Effort estimation Actual size Actual time 1. 0, 1 2. Effort = 1 * Estimated_size n Range = t 3. Range = t r 2 0.5

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J. Nawrocki, PSP, Lecture 7 Effort estimation begin.. end Actualsize Actualtime Historical data Lack of correlation between software size and actual time

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J. Nawrocki, PSP, Lecture 7 Effort estimation Actual size Actual time Effort = Estimated_size / P av time time 2 size size 2 P av = 3. P min = min { size i / time i } P max = max { size i / time i } P max = max { size i / time i } 4. Effort min = Estimated_size/P max Effort max = Estimated_size/P min Effort max = Estimated_size/P min

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J. Nawrocki, PSP, Lecture 7 Effort estimation No data about time You have to make a guess Actual size & actual time with r 2 < 0.5 Productivity-based estimation Actual size & actual time with r Effort estimate + range (inaccurate) Estimated size & actual time with r Effort estimate + prediction interval

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J. Nawrocki, PSP, Lecture 7 Plan of the lecture IntroductionIntroduction From the previous lectureFrom the previous lecture Effort estimationEffort estimation Multiple estimatesMultiple estimates Schedule estimatingSchedule estimating Progress trackingProgress tracking

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J. Nawrocki, PSP, Lecture 7 Multiple estimates Is the prediction interval [89.8, 195.2] correct?

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J. Nawrocki, PSP, Lecture 7 Multiple estimates Range(70%) = 32.3

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J. Nawrocki, PSP, Lecture 7 Multiple estimates Given: time estimates T 1, T 2,.., T n their standard deviations 1, 2,.., n. their standard deviations 1, 2,.., n. T total = T 1 + T T n total = n 2 total = n 2 T min (70%) = T total - total T max (70%) = T total + total T min (70%) = T total - total T max (70%) = T total + total T min (95%) = T total - 2* total T max (95%) = T total + 2* total T min (95%) = T total - 2* total T max (95%) = T total + 2* total In general:

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J. Nawrocki, PSP, Lecture 7 PROBE-generated predictions Task: Write a class C1: 144 LOC Write a class C2: 193 LOC Write a class C3: 318 LOC S total = 655 LOC S total = 655 LOCTask: Write a class C1: 144 LOC Write a class C2: 193 LOC Write a class C3: 318 LOC S total = 655 LOC S total = 655 LOC 1 = = = = = 5.69 = = (T i S i 1 ) 2 / (n-2) 2 = (T i S i 1 ) 2 / (n-2) T total = 0 + S total * 1 = (S total - s avg ) 2 (s i - s avg ) 2 (s i - s avg ) n + 1 Range = t Range(70%)= 16.3

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J. Nawrocki, PSP, Lecture 7 Plan of the lecture IntroductionIntroduction From the previous lectureFrom the previous lecture Effort estimationEffort estimation Multiple estimatesMultiple estimates Schedule estimatingSchedule estimating Progress trackingProgress tracking

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J. Nawrocki, PSP, Lecture 7 Schedule estimating Size Effort Calendar Schedule Availability factor

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J. Nawrocki, PSP, Lecture 7 Schedule estimating [h] [h] 1w2w3w4w5w6w7w Task 1Task 2Task 3

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J. Nawrocki, PSP, Lecture 7 Schedule estimating Project: ColorMap Data:

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J. Nawrocki, PSP, Lecture 7 Schedule estimating Project: ColorMap Data:

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J. Nawrocki, PSP, Lecture 7 Plan of the lecture IntroductionIntroduction From the previous lectureFrom the previous lecture Effort estimationEffort estimation Multiple estimatesMultiple estimates Schedule estimatingSchedule estimating Progress trackingProgress tracking

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J. Nawrocki, PSP, Lecture 7 Progress tracking Problem: how to track a progress when a sequence of tasks is rearranged? Earned Value Method: Each task is assigned a number of credit points. Each task is assigned a number of credit points. To earn the points assigned to a task, the task must be completed. To earn the points assigned to a task, the task must be completed. The points reflect time complexity and are normalised to 1000 points.The points reflect time complexity and are normalised to 1000 points. T1T2 T1T2

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J. Nawrocki, PSP, Lecture 7 Progress tracking Earned Value Method

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J. Nawrocki, PSP, Lecture 7 Progress tracking Project: ColorMap Data:

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J. Nawrocki, PSP, Lecture 7 Progress tracking Project: ColorMap Data:

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J. Nawrocki, PSP, Lecture 7 Ive forgotten about Ive forgotten about task T j !!! Progress tracking Adjusted EV: New_EV i = x1000 time i time j + time k

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J. Nawrocki, PSP, Lecture 7 Progress tracking Project: ColorMap Data:

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J. Nawrocki, PSP, Lecture 7 Summary Effort estimation is based on size estimation. Three cases: Best case Middle case Worst case Multiple estimates Schedule estimating Earned Value Method

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J. Nawrocki, PSP, Lecture 7 Further readings W. Humphrey, A Discipline for Software Engineering, Addison- Wesley, Reading, 1995, Chapter 5.

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J. Nawrocki, PSP, Lecture 7 Quality assessment 1. What is your general impression ? (1 - 6) 2. Was it too slow or too fast ? 3. Did you learn something important to you ? 4. What to improve and how ?

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