Effort and Schedule Estimation Copyright, 1999 © Jerzy R. Nawrocki [email protected]/mse/psp Personal Software.

Effort and Schedule EstimationEffort and Schedule Estimation

Copyright, 1999 © Jerzy R. Nawrocki

[email protected]@put.poznan.pl

www.cs.put.poznan.pl/~nawrocki/www.cs.put.poznan.pl/~nawrocki/mse/pspmse/psp

Personal Software Process Personal Software Process

Lecture 7Lecture 7

J. Nawrocki, PSP, Lecture 7

IntroductionIntroduction

Time & defect recordingTime & defect recording

Coding strd+Size measuremnt+PIPCoding strd+Size measuremnt+PIP

Size estimating + Test reportSize estimating + Test report

Task & schedule planningTask & schedule planning

Code & design reviewsCode & design reviews Design templates Design templates

Cyclic dev.Cyclic dev.

BaselineBaseline

PlanningPlanning

QualityQuality

CyclicCyclic


IntroductionIntroduction

begin .. end

500 LOC500 LOC


Plan of the lecturePlan of the lecture

• IntroductionIntroduction

• From the previous lectureFrom the previous lecture• Effort estimationEffort estimation• Multiple estimatesMultiple estimates• Schedule estimatingSchedule estimating• Progress trackingProgress tracking


From the previous lecture ..From the previous lecture ..

Humphrey, CMU, 1995Humphrey, CMU, 1995

PROPROxy-xy-BBased ased EEstimatingstimating

Objects as proxies Objects as proxies

StandardStandardcomponentcomponent

methodmethod

FuzzyFuzzylogiclogic

methodmethod

ProbeProbemethodmethod



4. Knowing:4. Knowing:• programming languageprogramming language• object typeobject type• size rangessize ranges• the number of methodsthe number of methods

estimate, using historical estimate, using historical data, size of each object.data, size of each object.



6. Apply linear 6. Apply linear regression to get regression to get estimated program estimated program size Y:size Y:

Y = Y = 11 X + X + 00

5 means 105 means 10



7. Using the 7. Using the t distributiont distribution and and standard standard deviationdeviation compute the compute the prediction intervalprediction interval for for a given percentage. a given percentage.

For 100% theFor 100% the

interval isinterval is

[0; + [0; + ]]



(X - x(X - xavgavg))22

(x(xii - x - xavgavg))22++

11

nn++11 Range = Range = tt

7c. Compute the range as follows:7c. Compute the range as follows:

Initial estimateInitial estimateobtained in Step 5obtained in Step 5



• IntroductionIntroduction• From the previous lectureFrom the previous lecture

• Effort estimationEffort estimation• Multiple estimatesMultiple estimates• Schedule estimatingSchedule estimating• Progress trackingProgress tracking


Effort estimationEffort estimation

begin .. endProgramsPrograms

written so farwritten so far

Historical dataHistorical data

It should take ...man month

to finish the project



begin .. end

EstimatedEstimatedsizesize

ActualActualtimetime




begin .. end




rr22 0.5 0.5rr22 0.5 0.5



Estimated sizeEstimated sizeActual timeActual time

1. 1. 00, , 11

2. Effort = 2. Effort = 11 * Estimated_size + * Estimated_size + 00

+ ...+ ...11

nn++11 3. Range = 3. Range = tt

rr22 0.5 0.5rr22 0.5 0.5

4. Effort4. Effortminmin = Effort - Range = Effort - Range



begin .. end




Lack of data or lack of correlation betweenLack of data or lack of correlation betweenestimated sizeestimated size and and actual timeactual time



begin .. end

ActualActualsizesize





begin .. end




rr22 0.5 0.5



Actual sizeActual sizeActual timeActual time

1. 1. 00, , 11

2. Effort = 2. Effort = 11 * Estimated_size + * Estimated_size + 00

+ ...+ ...11

nn++11 3. Range = 3. Range = tt

rr22 0.5 0.5



begin .. end




Lack of correlation betweenLack of correlation betweensoftware sizesoftware size and and actual timeactual time



Actual sizeActual sizeActual timeActual time

1.1.

2. Effort = Estimated_size / P2. Effort = Estimated_size / Pavav

timetime11 + .. + time + .. + time22

sizesize11 + .. + size + .. + size22 PPavav==

3. P3. Pminmin= min { size= min { sizeii / time / timeii } }

PPmaxmax= max { size= max { sizeii / time / timeii } }

4. Effort4. Effortmin min = Estimated_size/P= Estimated_size/Pmaxmax

EffortEffortmaxmax= Estimated_size/P= Estimated_size/Pminmin



No data about timeNo data about timeYou have to make a guessYou have to make a guess

Actual size & actual time with rActual size & actual time with r22 < 0.5 < 0.5Productivity-based estimationProductivity-based estimation

Actual size & actual time with rActual size & actual time with r22 0.5 0.5Effort estimate + range (inaccurate)Effort estimate + range (inaccurate)

Estimated size & actual time with rEstimated size & actual time with r22 0.5 0.5Effort estimate + prediction intervalEffort estimate + prediction interval



• IntroductionIntroduction• From the previous lectureFrom the previous lecture• Effort estimationEffort estimation

• Multiple estimatesMultiple estimates• Schedule estimatingSchedule estimating• Progress trackingProgress tracking


Multiple estimatesMultiple estimates

Task Estimatedhours

Lowerpredictioninterval L

Upperpredictioninterval U

Program 63.8 49.2 98.3Report 28.3 21.2 34.6Database 41.9 19.4 62.3Total 134.0 89.8 195.2

Task Estimatedhours

Lowerpredictioninterval L


Program 63.8 49.2 98.3Report 28.3 21.2 34.6Database 41.9 19.4 62.3Total 134.0 89.8 195.2

Is the prediction interval [89.8 , 195.2] correct?Is the prediction interval [89.8 , 195.2] correct?



Task Lowerpredictioninterval L


Standarddeviation(U - L)/2

VarianceStd_dev2

Program 49.2 98.3 24.55 602.70Report 21.2 34.6 6.70 44.89Database 19.4 62.3 21.45 460.10Total 89.8 195.2 1107.69

Task Lowerpredictioninterval L


Standarddeviation(U - L)/2

VarianceStd_dev2

Program 49.2 98.3 24.55 602.70Report 21.2 34.6 6.70 44.89Database 19.4 62.3 21.45 460.10Total 89.8 195.2 1107.69

1107.69 1107.69 32.3 32.3

32.3

Range(70%) = 32.3Range(70%) = 32.3



Given: time estimates TGiven: time estimates T11, T, T22, .., T, .., Tnn

their standard deviations their standard deviations 11, , 22, .., , .., nn..

TTtotaltotal= T= T11 + T + T22 + .. + T + .. + Tnn

totaltotal = = 1122 + + 22

22 + .. + + .. + nn22

TTminmin(70%) = T(70%) = Ttotaltotal - - totaltotal

TTmaxmax(70%) = T(70%) = Ttotaltotal + + totaltotal

TTminmin(70%) = T(70%) = Ttotaltotal - - totaltotal

TTmaxmax(70%) = T(70%) = Ttotaltotal + + totaltotal

TTminmin(95%) = T(95%) = Ttotaltotal - 2* - 2*totaltotal

TTmaxmax(95%) = T(95%) = Ttotaltotal + 2* + 2*totaltotal

TTminmin(95%) = T(95%) = Ttotaltotal - 2* - 2*totaltotal

TTmaxmax(95%) = T(95%) = Ttotaltotal + 2* + 2*totaltotal

In general:


PROBE-generated predictionsPROBE-generated predictions

Task:Task:

Write a class C1: 144 LOCWrite a class C1: 144 LOC



SStotaltotal = 655 LOC = 655 LOC

Task:Task:




SStotaltotal = 655 LOC = 655 LOC

Size Time123 14.286 9.6

321 46.1214 31.4186 13.898 11.3

118 9.4221 19.2

11= 0.1482= 0.1482

00= -5.95= -5.95

= 5.69= 5.6922= = (T (Tii - - 00 - S - Si i 11))2 2 / (n-2)/ (n-2)

TTtotaltotal= = 00 + S + Stotaltotal * * 11 = 91.13 = 91.13

(S(Stotaltotal - s - savgavg))22

(s(sii - s - savgavg))22++11nn++11 Range = Range = tt

Range(70%)= 16.3Range(70%)= 16.3



• IntroductionIntroduction• From the previous lectureFrom the previous lecture• Effort estimationEffort estimation• Multiple estimatesMultiple estimates

• Schedule estimatingSchedule estimating• Progress trackingProgress tracking


Schedule estimatingSchedule estimating

Size

Effort Calendar

Schedule

Availability factor



10 20 30 40 50 60 70 80 90 [h]10 20 30 40 50 60 70 80 90 [h]

1w1w 2w2w 3w3w 4w4w 5w5w 6w6w 7w7w

Task 1 Task 2 Task 3



Plan ActualWeek Direct

hoursCumulativehours

Directhours

Cumulativehours

1.06 20 208.06 25 45

15.06 15 60

Project: ColorMap Data: 23.05.1999Project: ColorMap Data: 23.05.1999



Plan ActualTask Hours Cumulative

hoursWeek Data

Detailed design 20 20 1.06Coding IO 25 45 8.06Coding graph 15 60 15.06

Project: ColorMap Data: Project: ColorMap Data: 23.05.199923.05.1999



• IntroductionIntroduction• From the previous lectureFrom the previous lecture• Effort estimationEffort estimation• Multiple estimatesMultiple estimates• Schedule estimatingSchedule estimating

• Progress trackingProgress tracking


Progress trackingProgress tracking

ProblemProblem: how to track a progress : how to track a progress when a sequence of tasks is when a sequence of tasks is rearranged? rearranged?

Earned ValueEarned Value Method: Method:

• Each task is assigned a number of Each task is assigned a number of credit points.credit points.

• To earn the points assigned to a To earn the points assigned to a task, the task, the task must be completedtask must be completed..

• The points reflect ‘The points reflect ‘timetime complexitycomplexity’ ’ and are and are normalisednormalised to 1000 points. to 1000 points.

T1T1 T2T2

T1T1T2T2



0

200

400

600

800

1000

1200

1 2 3 4

Weeks

Cu

mu

lati

ve E

V

Actual

Planned

0

200

400

600

800

1000

1200

1 2 3 4

Weeks

Cu

mu

lati

ve E

V

Actual

Planned

Earned Value MethodEarned Value Method



Plan ActualWeek Direct

hoursCum.hours

Cum.EV

Directhours

Cum.hours

Cum.EV

1.06 20 20 2508.06 25 45 670

15.06 15 60 1000

Project: ColorMap Data: 23.05.1999Project: ColorMap Data: 23.05.1999



Plan ActualTask Hours Cum.

hoursEV Week Date EV

Detailed design 20 20 333 1.06Coding IO 25 45 417 8.06Coding graph 15 60 250 15.06



I’ve forgotten aboutI’ve forgotten about

task Ttask Tjj !!! !!!


Adjusted EV:Adjusted EV:

New_EVNew_EVii= x1000= x1000timetimeii timetimejj + + time timekk



Plan ActualTask Hours Cum.

hoursEV Week Date EV

Detailed design 20 20 200 1.06Coding IO 25 45 250 8.06Coding graph 15 60 150 15.06Writing report 40 100 400 29.06



SummarySummary

Effort estimation is based on Effort estimation is based on size estimation.size estimation.

Three cases:Three cases:• Best caseBest case• Middle caseMiddle case• Worst caseWorst case



Earned Value MethodEarned Value Method


Further readingsFurther readings

W. Humphrey, A Discipline for W. Humphrey, A Discipline for Software Engineering, Addison-Software Engineering, Addison-Wesley, Reading, 1995, Chapter 5.Wesley, Reading, 1995, Chapter 5.


Quality assessmentQuality assessment

1. What is your general 1. What is your general impression ? (1 - 6)impression ? (1 - 6)

2. Was it too slow or too fast ?2. Was it too slow or too fast ?

3. Did you learn something 3. Did you learn something important to you ?important to you ?

4. What to improve and how ?4. What to improve and how ?

Effort and Schedule Estimation Copyright, 1999 © Jerzy R. Nawrocki [email protected]/mse/psp Personal Software.

Documents

t total total t max

effort range slide

t range

t n total

actual time slide

time estimates t

t distribution

effort estimate