1 Topic 5 Planning III – Estimating Software Size

1

Topic 5

PPlanning III lanning III –– EEstimating stimating SSoftware oftware SSizeize

2Topic 5 Planning III—Estimating Software Size

Lecture Overview

5.1 Background 5.2 Popular Estimating Methods 5.3 Proxy-based Estimating 5.4 The PROBE Size Estimating Method5.5 Object Categories 5.6 Estimating Considerations 5.7 Summary5.8 Exercises


5.1 Background5.1 Background


5.1 Background 1

Why Estimate Size? Size estimates allow you to make better plans.

Plan better for the resources, tasks, and schedule. The quality of a plan depends on that of the size estimate.

The more details you have, the more accurate the estimation will be. For a large software job, divide it into separate elements.

Size estimates assist you in tracking progress.

You can judge when the job scope changes. better measure the work.


Background 2

Estimating models in other fields have a large historical base. are widely used. generate detailed planning data. require a size estimate as input.


Project Planning Framework

The framework is shown in Figure 5.1. Size estimating: compare the design

elements with the historical size data to make estimates.

Accurate size estimation will lead to accurate resource estimation.


Fig. 5.1 Project Planning Framework

CustomerNeed

Delivered Product

Customer

Define theRequirements

Produce theConceptual

Design

Estimate theProduct Size

(Topic 5)

Estimate theResources

(Topic 6)

Produce theSchedule

(Topic 6)

Develop theProduct

ProcessAnalysis

ResourcesAvailable

HistoricalProductivity

Database

HistoricalSize

Database

TrackingReports

Items

Tasks

Size, Resource, Schedule

data

Management


Estimating Experience

Studies show that size estimation errors may be as high as 100% or even

more. Only 22% professionals make size estimates for cost

estimates. For software development, usually people have a larger

estimation error percentage (up to 400%) in early phase, and then the percentage declines (25% or less) in later phases.

Serious size estimation errors result in poor resource estimates and unrealistic project schedules.

Fortunately, the size estimation skill can be learned and improved.


Size Estimating Criteria

A widely used method should be structured and trainable. usable during all development and maintenance

phases. usable for all types of software product elements. suitable for statistical analysis. adaptable to the types of your future work. able to judge the accuracy of the estimates.


Size Estimating Principles 1

Estimating is an uncertain process. No one knows how big the product will be The earlier the estimate, the less is known Estimates can be biased by business and

other pressures (sometimes called ‘‘gutless’’ estimations)

Estimating is an intuitive learning process. Ability improves with experience Some people are better at estimating


Size Estimating Principles2

Estimating is a skill. Improvement will be gradual You may never get very good

The objective, however, is to get consistent -- at least “unbiased.” You will then understand the variability

of your estimates You seek an even balance between

under and over estimates


Size Estimating Errors - 12 Students

-200

-100

0

100

200

300

400

500

1 2 3 4 5 6 7 8 9 10

Program Number

% E

rro

r

Max

Class

Min


Size Estimating Principles 3

The principal advantages of using a defined estimating method are: You have known practices that you can work to

improve. It provides a framework for gathering estimating

data. By using consistent methods and historical data,

your estimates will get more consistent.


5.2 Popular Size Estimating 5.2 Popular Size Estimating MethodsMethods


5.2 Popular Size Estimating Methods

Four popular methods Wideband-Delphi Fuzzy-Logic Standard-Component Function-Point

Their concepts form the foundation of the PROBE (Proxy-based Estimating) method used with the PSP.


Wideband-Delphi Method 1

Originated by Rand Co. and refined by Boehm. Based on the Delphi process, which usually contains several

cycles. Several experts (estimators) and a moderator are involved in

the process. Each expert makes an independent estimate and submit it to

the moderator in each cycle. The moderator coordinates the estimation process for these

experts whose estimates are anonymous to all others. The process runs until experts’ estimates converge on a

consensus result.



The method’s process is as follows:1. A group of experts is each given the program’s

specifications and estimation forms.

2. They meet to discuss project goals, assumptions, and estimation issues.

3. They then each anonymously list project tasks and estimation size.

4. The estimates are given to the moderator, who tabulates the results and returns them to the experts, as illustrated in Figure 5.2.


Fig. 5.2 The Chart Used in Wideband-Delphi

0

Project: XYZEstimator: John DoeDate:4/1/94Here is the range of estimates from the 1st round:

20 40 60 80 100

X X* X! X X

X - estimatesX* - your estimateX! – median estimatePlease enter your estimate for the next round: ___SLOC.Please enter explain rationale for your estimate.



5. Only each expert’s personal estimate is identified; all others are anonymous.

6. The experts meet to discuss the results. They each review the tasks they have defined but not their size estimates.

7. The cycle continues at step 3 until the estimates converge to within an acceptable range.


Delphi Example 1

3 estimators are asked to estimate the product. Their initial estimates are:

A - 13,800 LOC B - 15,700 LOC C - 21,000 LOC

The coordinator then Calculates average estimate as 16,833 LOC Returns this with their original estimates to the

estimators


Delphi Example 2

The estimators then meet and discuss the estimates.

Their second estimates are A - 18,500 LOC B - 19,500 LOC C - 20,000 LOC

The coordinator then Calculates average estimate as 19,333 LOC Asks the estimators if they agree with this as the

estimate



Advantages can produce accurate results uses organization’s skills works for any size products.

Disadvantages relies on a few experts time consuming subject to common biases


Fuzzy-Logic Method 1

Estimators compare the planned program with prior programs and select the most appropriate size category.

Preparing for this method gather sufficient size data on previously developed

programs subdivide these data into size categories and

subcategories provide a meaningful number of program examples in

each size category and subcategory extend the size ranges up or down as you get further data

Table 5.1 shows the example.

Table 5.1 Example for Fuzzy-Logic Method

Subrange

Gross

Range

Very small

LOC

Small

LOC

Medium

LOC

Large

LOC

Very Large

LOC

Very small 1,1,48 1,514 2,000 2,630 3,467

Small 4,570 6,025 8,000 10,471 13,804

Medium 18,197 23,988 32,000 41,687 54,954

Large 72,444 95,499 128,000 165,958 218,776

Very Large 288,403 380,189 512,000 660,693 870,964


Fuzzy-Logic Method 2

Advantages based on relevant historical data easy to use requires no special tools or training provides reasonably good estimates in cases

where new work is like prior experience


A Fuzzy Logic Example 1

You have historical data on 5 programs as follows: a file utility of 1,844 LOC a file management program of 5,834 LOC a personnel record keeping program of 6,845

LOC a report generating package of 18,386 LOC an inventory management program of 25,943

LOC



You establish 5 ranges, as follows log(1844) = 3.266 log(25,943) = 4.414 the difference is 1.148 1/4th this difference is 0.287 the logs of the five ranges are thus spaced

0.287 apart the limits or these ranges are at 0.1435

above and below the midpoint of each range



The 5 size ranges are thus: very small - 1,325 to 2,566: file utility small - 2,566 to 4970: no members medium - 4,970 to 9,626: file management and

personnel record program large - 9,626 to 18,641: report generator very large - 18,641 to 36,104: inventory

management



Your new program has the following requirements: analyze marketing performance by product

line project the likely sales in each product

category allocate these sales to marketing regions and

time periods produce a monthly report of these projections

and the actual results



In comparing the new program to the historical data you make the following judgments: substantially more complex application than the file

management and personnel programs not as complex as the inventory management

program. appears to have significantly more function than the

report package. You conclude that the new program is in the lower

end of “very large,” or from 18 to 25 KLOC.


Fuzzy Logic - Summary

To make a fuzzy logic estimate:

1 - Divide the historical product size data into size ranges.

2 - Compare the planned product with these prior products.

3 - Based on this comparison, select the size that seems most appropriate for the new product.


Fuzzy-Logic Method Disadvantages

Fuzzy logic estimating requires a lot of data requires that the estimators be familiar with the

historically developed programs provides only a crude sizing not useful for new program types not useful for programs that are much larger or

smaller than the historical data


Standard-Component Method 1

Use organization’s historical data to determine the typical size of various types of components subsystems, modules, screens, reports, etc.

For a new project Judge the number of each type of components will

likely be in the project. Also determine the maximum and minimum

numbers of each type of components you could image.



Calculate the estimated number of each type with (4*likely number + max. number + min. number) / 6

The estimated size of each type is its estimated number * its typical size from historical

data Sum up the estimated size of all types to get the

total size.

Table 5.2 Example of Component Estimating

For modules, its estimated size = 932 * 17.5 = 16310.

Standard ComponentSLOC per

ComponentS M L

X=

(S+4*M+L)/6SLOC

SLOC 1

Object Instructions 0.28

Files 2,535 3 6 10 6.17 15,633

Modules 932 11 18 22 17.5 16,310

Subsystems 8,175

Screens 818 5 9 21 10.3 8,453

Report 967 2 6 11 6.17 5,963

Interactive Programs 1,769

Batch Programs 3,214

Total 46,359



Advantages based on relevant historical data easy to use requires no special tools or training provides a rough estimate range

Disadvantages must use large components early in a project limited data on large components hard to visualize component counts early in a

project


Function-Point Method 1

Count the numbers of the 5 types of basic functions that the commercial application program will likely need by reviewing its requirements. Inputs: screens or forms through which to add new data or

update existing data in the application Outputs: screens or reports that the application produces Inquires: screens used to ask for interrogation, assistance or

information Data files: logical collection of records that the application

updates Interface: e.g., files shared with other applications, shared

databases, parameter lists, …



Each type has a given weight. Calculate the function points of the

application multiply the number of each type by its weight. sum them up to get the total (unadjusted).

Use historical data on development cost and time per function point to make the estimates.


Table 5.3 Function-Point Categories

Basic Counts

Function Type Weights Total

Inputs ×4

Outputs ×5

Inquiries ×4

Logical Files ×10

Interfaces ×7

Unadjusted Total

Table 5.4 Function-Point Category Example

Basic Counts

Function Type Weights Total

8 Inputs 8 ×4 32

12 Outputs 12 ×5 60

4 Inquiries 4 ×4 16

2 Logical Files 2 ×10 20

1 Interfaces 1 ×7 7

Unadjusted Total

135



To improve the estimation, adjust the function points by 14 influence factors. The influence factor values are selected from 0

(very simple) to 5 (very complex) by the judgment of the estimator.

Sum up these values. Calculate the complexity multiplier

0.65+0.01*(sum of influence factor values) Calculate adjusted function points

complexity multiplier * unadjusted function points The adjustment is between ±35% Table 5.5 shows an example.


Table 5.5 An Example of Function-Point Influence Factors

Factor Influence

Data Communications 2

Distributed Functions 0

Performance objectives 3

Heavily used configuration 3

Transaction rate 4

On line data entry 4

End-user efficiency 3

On line update 2

Complex processing 3

Reusability 2

Installation ease 3

Operational ease 4

Multiple sites 5

Facilitate change 3

Sum of influence factors = 41

0.65+0.01*(sum of Influence Factors)=Complexity Multiplier= 1.06

Function Points=Complexity Multiplier*Unadjusted Function Points= 143



Advantages a well-documented method usable in the earliest requirements phase independent of programming languages, product

designs, development styles having a large body of historical data with an active user group



Disadvantages cannot directly count an existing product’s

function point content without historical data, difficult to improve

estimating skill not reflect language, design, and style differences designed for estimating commercial data

processing applications


5.3 Proxy-based Estimating 5.3 Proxy-based Estimating (PROBE)(PROBE)


5.3 Proxy-Based Estimating (PROBE)

In stead of direct estimating, a proxy is a substitute to help estimators judge product size. Proxies, for example, can be objects, screens,

files, scripts, document chapters, function points, and so on.


Criteria for a Good Proxy 1

The proxy size measurement (or estimate) should closely relate to the effort required to develop the product. Use the correlation method (see Topic 15) to determine

which proxy is a better predictor of product size or development effort.



The proxy content of a product should be automatically countable. Large amount of proxy data extracted from historical

data is needed to define new estimates. The proxy must be a physical entity that can be

precisely defined and algorithmically identified.



The proxy should be easy to visualize at the beginning of a project.

The proxy should be customizable to the special need of using organizations. Different product types may use different kind of

proxies to estimate. The proxy should be sensitive to any implementation

variations that impact development costs or efforts. for example, program languages, design styles,

application types


Objects as Proxies 1

Objects in OO programming languages are a good candidate as proxies because they closely relate to development effort or resources. Figures 5.3 and 5.4 show close correlation between

estimated object LOC (=Σobject size ) and actual program LOC. Linear regression formula: (See Topic 15 )

actual program LOC = β0 + estimate object LOC * β1

Figures 5.5 and 5.6 show high correlation and significance between estimated object LOC and actual development hours. Linear regression formula:

actual development hours = β0 + estimate object LOC * β1


Objects as Proxies 2

The PROBE method uses objects as proxies.

The PROBE method requires that estimators have historical data on the sizes of objects they have developed and that these data be divided into categories.


Fig. 5.3 Estimated Object LOC vs. Actual Program LOC (10 Pascal Programs)

0 100 200 300 400 500 600 700 800 900 1000

2000

1800

1600

1400

1200

1000

800

600

400

200

0

Act

ual

Pro

gram

L

OC

Estimated Object LOC


Fig. 5.4 Estimated Object LOC vs. Actual Program LOC (25 C++ Programs)

0 50 100 150 200 250 300 350

800

Act

ual

Pro

gram

L

OC


700

600

500

400

300

200

100

0


Fig. 5.5 Estimated Pascal Object LOC vs. Actual Development Hours with Correlation 0.934, Significance < 0.005

0 100 200 300 400 500 600 700 800 900 1000

200

180

160

140

120

100

80

60

40

20

0

Act

ual

Dev

elop

men

t H

ours



Fig. 5.6 Estimated C++ Object LOC vs. Actual Development Hours with Correlation 0.980, Significance < 0.005

0 50 100 150 200 250 300 350

60A

ctu

al D

evel

opm

ent

Hou

rs


50

40

30

20

10

0


Estimating Object Size with Fuzzy-Logic Method

Judge the size of each object on a per-object’s method basis with fuzzy-logic method.

1. decide the category of the object2. judge how many methods it likely will contain3. decide the size range it falls into4. estimated object size = (# of methods) * (LOC of the

size range) Table 5.6 shows object category sizes in LOC per-

object’s method For example, a medium-sized Pascal text object with

4 methods has about 66 (=16.48*4) LOC.


Table 5.6 Object Category Sizes in LOC per Method

C++ Object Size in LOC per Method

CategoryVery

SmallSmall Medium

Large

Very

Large

Calculation

2.34 5.13 11.25 24.66 54.04

Data 2.60 4.79 8.84 16.31 30.09

I/O 9.01 12.06 16.15 21.62 28.93

Logic 7.55 10.98 15.98 23.25 33.83

Set-up 3.88 5.04 6.56 8.53 11.09

Text 2.75 8.00 17.07 36.41 77.66

Object Pascal Object Size in LOC per Method

CategoryVery

Small

Small Medium LargeVery

Large

Control 4.24 8.68 17.79 36.46 74.71

Display 4.72 6.35 8.55 11.50 15.46

File 3.30 6.23 11.74 22.14 41.74

Logic 6.41 12.42 24.06 46.60 90.27

Print 3.88 5.86 10.15 17.59 30.49

Text 4.63 8.73 16.48 31.09 58.62


5.4 The PROBE Size 5.4 The PROBE Size Estimating MethodEstimating Method


5.4 The PROBE Size Estimating Method

The PROBE size estimating procedure is shown in Figure 5.7.

In the procedure, a size estimating template, shown in Table 5.7, will be used.

Use the template to instruct how to estimate program size with the PROBE method and linear regression.


Fig. 5.7 The Procedure of PROBE Method

Start

Step 1:Conceptual

design

Step 3:Calculate projected and

modified LOC.

Step 4:Estimate

program size.

Step 5:Calculate

prediction interval.

Number ofmethods

Objecttype

Relativesize

Reusecategories

Step 2:Identify object

Size Estimate

Table 5.7 Size Estimating Template and example 1

Table 5.7 Size Estimating Template and example 2


Size Estimating Template 1

Base Program: the program you will enhance and perform any changes to it. Base Size (B): the LOC of the base program LOC Deleted (D): the LOC to be deleted from the

base program LOC Modified (M): the LOC to be modified in the

base program



Projected LOC (P): includes total base additions LOC (BA) and total new objects LOC (NO) Base Additions: the new functions to be added to the

base program New Objects: the new objects to be added to the base

program New Reused Objects: a new object planned to develop

and general enough to put into the reuse library (Note: mark an * with the new reused objects)

Reused Objects (R): the objects taken from the reuse library



Calculations Using actual new and changed LOC and estimated

Object LOC (i.e., P+M) from historical data to obtain regression parameters β0, β1

Estimated New and Changed LOC (N): use regression parameters to calculate

N = β0 +β1 * (P + M) Estimated Total LOC (T): the estimated size of the

final program



Prediction Range: using t distribution (See Section 15.5.2), a desired prediction interval percent, and the data for linear regression to calculate

Lower/Upper Prediction Interval: the interval (N ± Range) within which the actual new and changed LOC is likely to fall

Prediction Interval Percent: the percentage that the actual new and changed LOC is likely to fall within the interval


PROBE Step 1 – Conceptual Design

The conceptual design establishes a preliminary design approach and names the expected product objects and their functions.

For an accurate estimate, estimators must refine the conceptual design to the level of objects.


PROBE Step 2 – Identify Objects

Determine object type and size if it is a new object, use fuzzy-logic method (see

Section 5.3) # of methods (judging size using a per-method basis) object type relative size LOC per method estimated object size = (# of method) * (LOC per method)

(Note: mark an * with the new reused objects) if the object is taken from the reuse library, determine

reuse object categories object LOC


PROBE Step 3 – Calculate Projected and Modified LOC

Projected LOC (P) = Total Base Additions LOC (BA) +

Total New Objects LOC (NO) Using actual new and changed LOC and

estimated object LOC (i.e., P+M) from historical data to obtain regression parameters β0, β1

Estimated New and Changed LOC (N) = β0 +β1 * (P+M)


PROBE Step 4 – Estimate Program Size

Estimated program size = Base Size (B) + Estimated New and Changed LOC (N) + Reused Total LOC (R) – LOC Deleted (D) – LOC Modified (M)

LOC Modified (M) is subtracted because it is counted twice: one in Base Size (B) and the other in Estimated New and Changed LOC (N).


PROBE Step 5 – Calculate Prediction Interval 1

The purpose is to assess the quality of the estimate. Use t distribution, a desired prediction interval

percent, and the data for linear regression calculation to get Prediction Range Prediction Interval [LPI, UPI]

Within the prediction interval, the actual new and changed LOC is likely to fall with the selected percent.


PROBE Step 5 – Calculate Prediction Interval 2

Prediction Interval: [LPI, UPI] Lower Prediction Interval (LPI) =

Estimated New and Changed LOC (N) - Range Upper Prediction Interval (UPI) =

Estimated New and Changed LOC (N) + Range


PROBE Step 5 – Calculate Prediction Interval 3 Prediction Range: using the data for linear

regression calculation in Step 3

Where xi is the estimated object LOC (i.e., P+M) in historical dataxavg is the average of xi

xk is the estimated object LOC (i.e., P+M) in the new programyi is actual new and changed LOC in historical data

niavgi

avgk

xx

xx

nntRange

..1

2

2

)(

)(11)2,2/(

n

iii xy

nVariance

1

210

2 )()2

1(


5.5 Object Categories5.5 Object Categories


5.5 Object Categories

The PROBE method uses objects as proxies. It needs object categories and their sizes to judge the size of the new objects.

Table 5.6 shows the example that we want to produce.

for C++ and Object Pascalobject sizes per method are divided into 5 size

ranges: very small, small, medium, large, and very large.


Object Size Distribution

Assume historical object data are normally distributed.

Figure 5.8 shows the relation between the normal distribution and the size ranges.


Fig. 5.8 Normal Distribution with Size Ranges

σ


Approaches

Two approaches If the size data are close to normal distribution,

use normal distribution in the calculation . Otherwise, use normal distribution of natural log in

the calculation.


Approach I: Procedure with Original Data

Use historical data on objects divide them into categories calculate the size range midpoints for each

category. use statistical techniques of normal distribution in the

calculation

The following is the procedure:


Step 1: Classification

Divide objects in historical data into categories.

Basic object categories logic, control I/O, files, display data, text, calculation set-up, error handling


Step 2: Calculating LOC per Method

For each category, calculate the LOC per method of each object.

Table 5.9 shows the example for 13 objects belonging to the object category, text.


Table 5.8 Pascal Text Object LOC per Method

Object Name Number of Methods Object LOC LOC per Method

each_line 3 31 10.333

each_char 3 18 6.000

list_clump 4 87 21.750

character 3 87 29.000

single_character 3 25 8.333

single_read 3 18 6.000

list_clp 4 89 22.250

char 3 85 28.333

single_char 3 37 12.333

converter 10 558 55.800

string_manager 4 82 20.500

string_builder 5 82 16.400

string_decrementer 10 230 23.000


Step 3: Calculating Standard Deviation

For each category, calculate the variance and standard deviation of the values from Step 2. Variance = σ2 = (1/n) ∑i=1..n(xi – xavg )2

Standard Deviation = √ of Variance =σ

where xi = (LOC per method) of each object of one specific category; xavg denotes the average.

The following table shows the example.


Table 5.9 Pascal Text Object Standard Deviation

Object Name LOC per Method (LOC-LOCavg)2

each_line 10.333 93.249

each_charr 6.000 196.072

list_clump 21.750 3.054

chracter 29.000 80.954

single_character 8.333 136.171

single_read 6.000 196.072

list_clp 22.250 5.051

char 28.333 69.402

single_char 12.333 58.817

converter 55.800 1281.456

string_manager 20.500 0.247

string_builder 16.400 12.978

string_decrementer 23.000 8.985

Total 260.033 2142.753

Average 20.003

Variance=Total/n = 164.827

Standard Deviation = 12.839Variance

2


Step 4: Calculating Size Range Midpoints

For each category, use the average xavg and standard deviation σ to calculate the size range midpoints VL = xavg + 2σ

L = xavg + σ

M = xavg

S = xavg – σ

VS = xavg - 2σ


Negative Size Range Midpoints

Table 5.10 shows the example of the size range midpoints and the 13 object sizes per method.

However, in this example, the VS midpoint is a negative number, which is not what we want and means that the object data are not normally distributed.


Table 5.10 Pascal Text Object and Size Ranges

Size Ranges Range Midpoint

LOC

Object

LOC/Method

VS -5.68

6.000

6.000

S 7.16

8.333

10.333

12.333

16.400

M 20.00

20.500

21.750

22.250

23.000

28.333

29.000

L 32.84

VL 45.68

55.800


Approach II: Procedure with Natural Log of Original Data

The trick to handle the negative size range midpoint is to calculate the natural log of the data.

The following is the procedure with the first 2 steps same as the previous procedure.


Step 3: Calculating the Natural Log and Average

For each category, calculate the natural log (ln) of the LOC per method of each object, and the average, avgln, of these log values.

Table 5.11 is the example for object category, text.


Table 5.11 Pascal Text Object ln (LOC per Method)

Object Name LOC per Method In (LOC per Method) (In LOC-In LOCavg)2

each_line 10.333 2.335 0.2173

each_charr 6.000 1.792 1.0196

list_clump 21.750 3.080 0.0773

chracter 29.000 3.367 0.3201

single_character 8.333 2.120 0.4641

single_read 6.000 1.792 1.0196

list_clp 22.250 3.102 0.0905

char 28.333 3.344 0.2943

single_char 12.333 2.512 0.0836

converter 55.800 4.022 1.4890

string_manager 20.500 3.020 0.0479

string_builder 16.400 2.797 0.0000

string_decrementer 23.000 3.135 0.1115

Total 260.033 36.419 5.2348

Average 20.003 2.802

Variance 164.827 0.4027

Standard Deviation 12.839 0.6346

InVL=2.802+1.269 4.071 VL=e4.071= 58.62

InL=2.802+.635 3.437 L=e3.437= 31.09

InM=2.802 2.802 M=e2.802= 16.48

InS=2.802-.635 2.167 S=e2.167= 8.73

InVS=2.802-1.269 1.533 VS=e1.533= 4.63


Step 4: Calculating Standard Deviation

For each category, calculate the variance and standard deviation of these log values. Variance = σ2 = (1/n) ∑i=1..n(xi – xavg )2

Standard Deviation = √ of Variance =σ

where xi = ln (LOC per method) of each object of a category.


Step 5: Calculating the Log of Size Range Midpoints

For each category, use average of log values and standard deviation to calculate the log of size range midpoints ln(VL) = avgln + 2σ

ln(L) = avgln + σ

ln(M) = avgln

ln(S) = avgln – σ

ln(VS) = avgln - 2σ


Step 6: Calculating the Antilog

For each category, calculate the antilog to get the midpoints of the size ranges VL = eln(VL)

V = eln(L)

M = eln(M)

S = eln(S)

VS = eln(VS)

Table 5.12 shows the example of the size range midpoints and the 13 object sizes per method.


Table 5.12 Pascal Text Objects and Size Ranges

Size Ranges Range Midpoint LOC Object LOC/Method

VS 4.63

6.000

6.000

8.333

S 8.73

10.333

12.333

16.400

M 16.48

20.500

21.750

22.250

23.000

28.333

29.000

L 31.09

55.800

VL 58.62


5.6 Estimating Considerations5.6 Estimating Considerations


5.6 Estimating Considerations 1

The PSP estimating objective is to improve your estimating ability by tracking and analyzing your estimates.

If your estimating process is stable, the linear regression method, based on the historical data gathered from consistently biased estimates, can make an accurate bias adjustment.

While you gain more experience and your process evolves, you should adjust your statistical calculations to include only the newer and more representative data and drop the old ones.


Estimating Considerations 2

Whenever your linear regression parameters appear unreasonable (e.g., β1 far from 1.0, large β0 ), use the averaging method to estimate. This method uses a ratio to adjust size or time

based on historical averages. estimated program LOC = estimated object LOC * ratio

where ratio = historical average =

∑(final program LOC) / ∑ (estimated object LOC)



To use the linear regression method, you must have at least three programs for which you have made object LOC estimates.

If you have at least three programs but enough historical estimate data, obtain β0 and β1 from your actual size data

If you do not have enough historical data but at least one program, use the averaging method



You can learn to reduce the estimating bias by comparing, during postmortem, estimates made at each phase with their actual size.

To estimate unprecedented products, the best answer is to resist making firm estimates until you have completed a feasibility study and build some prototypes.


5.7 Summary5.7 Summary


5.7 Summary 1

Accurate size estimate will help you to make better development plans.

Size estimating skill improve with practice. A defined and measured process provides a

repeatable basis for improvement.


Summary 2

With PROBE, estimates are based on one of following four methods:

Method A: regression with estimated object LOCMethod B: regression with estimated new and

changed LOCMethod C: size or time adjustment based on

historical averages (the averaging method)Method D: engineering judgment


Summary 3

Method A: regression with estimated object LOC

use the relationship between estimated object LOC and

actual new and changed LOC actual development time

The criteria for using this method are3 or more data points that are correlated (r2 > 0.5)Reasonable regression parameters


Summary 4

Method B: regression with estimated new and changed LOC

use the relationship between estimated new and changed LOC and

actual new and changed LOC actual development time

The criteria for using this method are3 or more data points that are correlated (r2 > 0.5)Reasonable regression parameters


Summary 5

Method C: adjusting size or time based on historical averages.

the averaging methodeasy to useused when linear regression parameters (β0, β1)

appear unreasonable (that is, methods A and B can not be used)

assuming that there is no fixed overhead The criteria for using this method are

At least one data point is required.


Summary 6

Method D: engineering Judgment with estimated object LOC

used in absence of historical datausing judgment from estimated object LOC to

estimate actual new and changed LOC actual development time

used when methods A, B, and C can not be used


5.8 Exercises5.8 Exercises


Program 3A 1

Use PSP0.1 (see Appendix in Topic 4) to write program 3A.

Program 3A Requirements: Write a program to count

the total program LOC the total LOC in each object the number of methods in each object

If an object-oriented program is not used as the input, count the total program LOC the total LOC in each procedure or function


Program 3A 2

Use the counting standard produced by report exercise R1.

It is acceptable to enhance program 2A or to reuse some of its methods, procedures, or functions in developing this program.

Program 3A Testing: Thoroughly test the program. At a minimum, test program 1A, 2A, and 3A. Prepare a test report that includes a table as the format

in Table 5.11. Produce a report on the defect fix times for programs

1A, 2A, and 3A.

Table 5.11 Test Result Format Program 3A

Program number

Object Name Number of Methods

Object LOC Total Program LOC

1A ABC 3 86

DEF 2 8

GHI 4 92

212

2A …

a) Format for object-oriented program designs

Program number

Procedure/

Function Name

Number of Methods

Procedure/

Function LOC

Total Program LOC

1A ABC 1 86

DEF 1 8

GHI 1 92

212

2A …

b) Format for non object-oriented program designs


Order to Submit Assignment:

Cover sheet: Exercise #, Name, SID PSP0.1 Project Plan Summary PIP form, including lessons learned Time Recording Log Defect Recording Log Source program listing Report R1 LOC counting standard (if modified) Report R2 coding standard (if modified) Report R3 defect analysis report Test Results

Program 3A 3


Program 4A 1

Use PSP1 (see Appendix in this topic) to write program 4A.

Program 4A Requirements: Write a program to calculate the linear regression

size-estimating parameters:

avgavg

n

iavgi

n

iavgavgii

xy

xnx

ynxyx

1

22

1

)(


Program 4A 2

Use the historical data of a set of n programs: object LOC, xi, and new and changed LOC, yi.

Enhance the linked list of program 1A to hold the n data records, where each record has 2 real numbers.


Program 4A 3

Program 4A Testing: Thoroughly test the program. At a minimum, test it with the 3 cases shown in Table

5.12: use the data for estimated object LOC and actual new and

changed LOC. use the data for estimated new and changed LOC and actual

new and changed LOC. use the data, estimated new and changed LOC and actual

new and changed LOC, you have gathered for the programs 2A, 3A and 4A you have developed.

Prepare a test report that includes a table as the format in Table 5.13.

Table 5.12 Size Estimating Regression Data

Program Number Estimated Object LOC Estimated New and Changed LOC

Actual New and Changed LOC

1 130 163 186

2 650 765 699

3 99 141 132

4 150 166 272

5 128 137 291

6 320 355 331

7 95 136 199

8 945 1206 1890

9 368 433 788

10 961 1130 1601

Sum 3828 4632 6389

Average 382.8 463.2 638.9

Table 5.13 Test Result Format Program 4A

Test Expected Results Actual Results

β0 Β1 Β0 Β1

Table D8:

Estimated Object versus

Actual New And Changed

LOC

-22.55 1.7279

Table D8:

Estimated New and Changed LOC versus Actual New and Changed LOC

-23.92 1.4310

Program 2A, 3A, 4A:

Estimated New LOC versus Actual New and Changed LOC

NA NA


Appendix PSP 1Appendix PSP 1


Report R3 RequirementsReport R3 Requirements


Requirements for Report 3

The objectives of the defect analysis are to help you understand the Density and type of defects introduced and found

while developing programs. Importance of carefully gathering, recording, and

reporting process data.


Report 3 Requirements 1

Analyze the defects found in development the initial programs.

Produce a report that includes these table. Defect density Compile and test phase defect density Average defect fix time by phase injected and

removed


Report 3 Requirements 2

To prepare the defect analysis report, you will need the following project plan summary data for programs 1A through 3A. new and changed LOC defect injected and removed for each phase

From the defect logs, you will need counts and fix times for all defects. Injected in design and found in compile or test Injected in coding and found in compile or test Injected in design or coding founded in compile or test


Example Defect Analysis Report 3 Defect Densities Compile and Test Defects

Program Number

New and Changed LOC

Total Defects Defects per KLOC

Defects found in compile

Compile defects per KLOC

Defects found in test

Test defects per KLOC

1 111 17 153 9 81 2 18

2 88 6 68 6 68 0 0

3 103 10 97 4 39 5 49

4 77 8 104 3 39 5 49

5 81 8 99 0 0 0 0

6 124 8 65 3 24 0 0

7 175 24 137 5 29 2 11

8 117 9 77 1 9 0 0

9 153 16 105 0 0 1 7

10 224 15 67 2 9 1 4

Totals 1253 121 97 33 26 11 9


Example Defect Analysis Report 3

Defect Fix Times

Defects found in compiling

Defects found in testing

Total defects found

Defects injected in designing

Total fix time 1 137 256

Total defects 1 4 48

Average fix time

1 34 5

Defects injected in coding



Average fix time

3 3 2

Total defects injected



Average fix time

3 14 4

PSP1 Process Script 1

Phase Number

Purpose To guide you in developing module-level programs

Entry Criteria Problem descriptionPSP1 Project Plan Summary formSize Estimating TemplateHistorical estimate and actual size dataTime and Defect Recording LogsDefect Type StandardStop watch (optional)

1 Planning Produce or obtain a requirements statementUse the PROBE method to estimate the total new and changed LOC required.Complete the Size Estimating Template.Estimate the required development timeEnter the plan data in the Project Plan Summary formComplete the Time Recording Log

2 Development Design the programImplement the designCompile the program and fix and log all defects foundTest the program and fix and log all defects foundComplete the Time Recording Log

PSP1 Process Script 2

Phase Number

Purpose To guide you in developing module-level programs

3 Postmortem Complete the Project Plan Summary form with actual time and defect data

Exit Criteria A thoroughly tested programCompleted Project Plan Summary with estimated and actual dataCompleted Size Estimating TemplateCompleted Test Report TemplateCompleted PIP formsCompleted Defect and Time Recording Logs

PSP1 Planning Script 1

Phase Number

Purpose To guide the PSP planning process

Entry Criteria Problem descriptionPSP1 Project Plan Summary formSize Estimating TemplateHistorical estimate and actual size dataTime Recording Logs

1 Program Requirements

Produce or obtain a requirements statement for the program.Ensure the requirements statement is clear and unambiguous.Resolve any questions.

2 Size Estimate Produce a program conceptual design.Use the PROBE method to estimate the new and changed LOC required to develop this program.Estimate the base, added, deleted, modified, and reused LOC.Complete the Size Estimating Template and Project Plan Summary.

PSP1 Planning Script 2

Phase Number

Purpose To guide the PSP planning process

3 Estimate Resources

Based on the time required per LOC on previous programs, make your best estimate of the time required to develop this program.Using the To Date % from the most recently developed program as a guide, describe the development time over the planned project phases.

Exit Criteria A documented requirements statementThe program conceptual designCompleted Size Estimating TemplateA completed Project Plan Summary with estimated program size and development time dataCompleted Time Recording Logs

PSP1 Development Script 1

Phase Number

Purpose To guide the Development of small programs

Entry Criteria

Requirements statementProject Plan Summary with planned program size and development timeTime and Defect Recording LogsDefect Type Standard and Coding Standard

1 Design Review the requirements and produce a design to meet themRecord time in Time Recording Log

2 Code Implement the design following Coding StandardRecord in the Defect Recording Log any requirements or design defects foundRecord time in Time Recording Log

3 Compile Compile the program until error freeFix all defects foundRecord defects in the Defect Recording LogRecord time in Time Recording Log

PSP1 Development Script 2

Phase Number

Purpose To guide the Development of small programs

4 Test Test until all tests run without error.Fix all defects found.Record defects in the Defect Recording Log.Record time in Time Recording Log.Complete a Test Report Template on the tests conducted and results obtained.

Exit Criteria

A thoroughly tested program that conforms to the Coding StandardCompleted Test Report TemplateCompleted Defect and Time Recording Logs

Phase Number

Purpose To guide the PSP postmortem process

Entry Criteria

Problem description and requirements statementProject Plan Summary form with planned program size and development timeCompleted Test Report TemplateCompleted Time Recording LogsCompleted Defect Recording LogA tested and running program that conforms to the Coding Standard

1 Defects Injected

Determine from the Defect Recording Log the number of defects injected in each PSP1 phaseEnter this number under Defects Injected – Actual on the Project Plan Summary

2 Defects Removed

Determine from the Defect Recording Log the number of defects removed in each PSP1 phaseEnter this number under Defects Removed – Actual on the Project Plan Summary

3 Size Count the LOC in the completed program.Determine the base, reused, deleted, modified, added, total, total new and changed, and new reused LOC.Enter these data on the Project Plan Summary.

PSP1 Postmortem Script 1

Phase Number

Purpose To guide the PSP postmortem process

4 Time Review the completed Time Recording LogEnter the total time spent in each PSP1 phase under Actual on the Project Plan Summary form

Exit Criteria

A fully tested program that Conforms to the Coding StandardCompleted Test Report TemplateCompleted Project Plan Summary formCompleted PIP forms describing process problems, improvement suggestions, and lessons learned.Completed Defect and Time Recording Logs

PSP1 Postmortem Script 2



Test Report Template

PSP1 Project Plan Summary 1

PSP1 Project Plan Summary 2

Documents

1 Topic 5 Planning III – Estimating Software Size