Test Estimation in Practice

TQ
PM Tutorial
10/14/2014 1:00:00 PM
"Test Estimation in Practice"
Presented by:
Rob Sabourin
AmiBug.com
Brought to you by:
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Rob Sabourin
AmiBug.com
Rob Sabourin, P. Eng., has more than thirty years of management experience leading teams of software
development professionals. A well-respected member of the software engineering community, Rob has managed,
trained, mentored, and coached hundreds of top professionals in the field. He frequently speaks at conferences and
writes on software engineering, SQA, testing, management, and internationalization. Rob wrote I am a Bug!, the
popular software testing children's book; works as an adjunct professor of software engineering at McGill University;
and serves as the principle consultant (and president/janitor) of AmiBug.Com, Inc. Contact Rob at Contact Rob
at rsabourin@amibug.com.

Introduction
TEST Estimation IN
PRACTICE

Course
timing
Course
timing
Electron
ic
devices
Electron
ic
devices
Smokin
g
Smokin
g
MealsMeals
Faciliti
es
Faciliti
es
Break
s
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Administrivia
6© 2012 SQE Training V3.0

Introductions
Name
Company
Name
Business
Environment
Hardware
Software
Experience
Testing
Management
Personal Course Objective(s)
Name
Company
Name
Business
Environment
Hardware
Software
Experience
Testing
Management
Personal Course Objective(s)

Course Agenda
1. Estimation
2. Estimation Techniques

Estimation
Estimate:
– A tentative evaluation or rough calculation
– A preliminary calculation of the cost of a
project
– A judgment based upon one’s impressions;
opinion
—The American Heritage Dictionary
It is very difficult to make a vigorous, plausible, and job-
risking estimate that is derived by no quantitative method,
supported by little data and certified chiefly by hunches of the
managers.
— Fred Brooks

The best estimatesThe best estimates
Represent the collective wisdom of practitioners
(testers, developers, users, etc.) and have their buy-in
Provide specific, detailed catalogs of the costs,
resources, tasks, and people involved
Present, for each activity estimated, the most likely
cost, effort, and duration
Estimation: the creation of an approximate
target for costs and completion dates
Estimation: the creation of an approximate
target for costs and completion dates
Test Estimation

Factors that can influence cost, effort, and duration include:Factors that can influence cost, effort, and duration include:
Required level of quality of the system
Size of the system to be tested
Historical data
Process factors (process maturity, etc.)
Material factors (tools, data, etc.)
People factors (skills, experience, managers, etc.)
Test Estimation (cont.)

Test Estimation (cont.)
• Delivery of estimates should include
justification
• Negotiation and re-work of estimates is
normal
• Final estimates represent a balance of
organizational and project goals in the areas of
quality, schedule, budget, and features

How Good an Estimator Are You?
Low Estimate High Estimate
___________ to ____________
.
.
.
© Steve McConnell, Software Estimation, Microsoft Press, 2006
Surface temperature of the sun
Latitude of ShanghaI
Area of the Asian continent
Birth year of Alexander the Great
Total value of US currency in 2004
Total volume of the Great Lakes
Worldwide box office receipts of Titanic
Total length of Pacific Ocean coastline
Heaviest blue whale ever recorded
# of books published in US since 1776

How Good an Estimator Are You?
• Surface temperature of the sun 10,0000 F / 6,000 C
• Latitude of ShanghaI 31° 10′ N
• Area of the Asian continent 17,212,000 sq mi
• Birth year of Alexander the Great 356 BC
• Total value of US currency in 2004 $719.9 Billion
• Total volume of the Great Lakes 5,500 cu mi
• Worldwide box office receipts of Titanic $1.835 billion
• Total length of Pacific Ocean coastline 84,300 miles
• Heaviest blue whale ever recorded 380,000 pounds
• Number of books published in US since 1776 22 million

How Good Is Our Industry (at Estimating)?
• Tata: 62% of projects fail to meet schedule
49% have budget overruns
• Moiokken and Jorgensen: 30-40%
overruns

Class Discussion
Why is estimating not done well?
Your top five reasons:
1) Too many variables____________________
2) ____________________________________
5) ____________________________________
6) ____________________________________
5) ____________________________________

Why Estimates Are Inaccurate ― Part I
• Lack of estimating experience
• Lack of historical data on which to base estimates
• Lack of systematic estimation process, sound techniques, or
models suited to the project
• Failure to include essential activities and products within the
scope of the estimates
• Unrealistic expectations or assumptions
• Failure to recognize and address the uncertainty inherent in
project estimates
Practical Software Measurement
Addison-Wesley, 2001

Why Estimates Are Inaccurate ― Part II
• Lack of education and training
• Confusing the target with the estimate
• Hope-based planning
• Inability to communicate and support estimates
• Incomplete, changing, and creeping requirements
• Quality surprises (test and re-fix)
—adapted from Linda M. Laird
The Limitations of Estimation

Bohem’s Cone of Uncertainty

NHC Track Forecast Cone

“Testing” Track Forecast Cone
Best case
Expected case
Worst case
T i m e
Tasks
(or why it is important to constantly re-estimate)

What would have to happen to deliver this in four
weeks?
What would have to happen to deliver this in four
weeks?
What should the estimate have been?What should the estimate have been?
The Fantasy Factor
Weeks
Today
0 1 2 3 4 5 6 7 8 9
1st 3rd2nd

Tim
e
Tim
e
SizeSize
Quali
ty
Quali
ty
Res
our
ces
Res
our
ces
Estimation
1, 2, 3, or 4 Variables + Many Modifiers:
If it’s not variable, then it’s fixed.

Time vs. Resources
=

Test Estimation Techniques ― Examples
• Intuition and guess
• Work-breakdown-structures
• Three-point estimates
• Company standards and norms
• % of project effort or staffing
• Industry averages and predictive models (e.g., FP, TPA )
• Team estimation sessions
– Wideband Delphi
– Story point sizing
– Poker estimation
– T-shirt sizing

What do intuition and guess
imply?
What do intuition and guess
imply?
No formal estimation process
Based on the experience of the team
member(s) usually without the benefit of
recorded metrics
Intuition and Guess

When would we use this technique?When would we use this technique?
Experienced staff members
Little or no recorded metrics available
Great accuracy is not required
Same type of project we’ve done in the
past
Or at the other extreme, we don’t have a
clue (often called a WAG or SWAG)
Intuition and Guess (cont.)

Project LevelProject Level
Phase
Level
Phase
Level
Task LevelTask Level
Phase
Level
Phase
Level
------
------
------
------
------
Phase
Level
Phase
Level
Phase
Level
Phase
Level
Work Breakdown Structure

When the test team lacks credibility with their
estimates
When we are doing an estimate for a project that is
dissimilar to projects we have done in the past
When the project is too large to get our “arms
around it” (e.g., it is too big to easily see the big
picture)
Work Breakdown Structure (cont.)

When would we not use this technique?When would we not use this technique?
When the project has lots of turbulence!

Approach:Approach:
Develop goal
Break goal into deliverables
Break deliverables into activities
Decompose work into small manageable
components that are of sufficient detail to
allow you to make estimates of time and/or
resources
Work Breakdown Structure ― Top Down

Testing for Completeness
• Status of a task is measurable
• Start and end events are clearly defined
• Further breakdown makes no sense
• Each activity has a deliverable
• Time or resources for an activity can be estimated
• Duration of activity is within acceptable limits
• Each activity is independent
• Each activity forms a unique package of work that could
be outsourced

• Level of detail guidelines:
– No single activity or group of activities should
exceed 80 hours of effort*
– No activity or group of activities should exceed
the reporting period (e.g., if the reporting period
is weekly, no activity should exceed one week)
– “If it makes sense” rule: The duration of each
activity must pass the common sense rule

Three–Point Estimates
• Three-point estimates is an analytical technique using
three cost or duration estimates:
– Optimistic
– Most likely
– Pessimistic
• This technique is applied to improve the accuracy of
the estimates of cost or duration
• Three-point estimation techniques can be employed
with other estimating techniques such as Delphi

% of Project Effort or Staffing
• Based on the premise that there is a
predictable correlation between development
effort/time and test effort/time
• The preferred/“dictated” method of
estimation in some organizations
• Sometimes associated with developer/tester
ratios
• One method recognized by the ISTQB

When Would We Use This Technique?When Would We Use This Technique?
An organization is producing more or less the same type of
product on an ongoing basis
The organization (development and testing) uses consistent
practices and has a stable staff
A crude estimate is needed
It is mandated
It works
% of Project Effort or Staffing

Issues:Issues:
We are basing our estimates on development
estimates which are probably suspect
Some products that are relatively easy to develop
may be difficult to test and vice versa
This technique may not take into account test
automation, etc.
This method may not take the stated quality goals
into account
Test Estimates Based on Development Estimates

Class Discussion
What is the ideal developer/tester ratio?
= ?

McConnell Developer to Tester Ratio
Environment Relative Importance Ratio
(Developer:Tester Ratio)
Business Systems 3:1 to 20:1
Shrink Wrap 1:1 to 5:1
Scientific Projects 5:1 to 20:1
Systems Projects 1:1 to 5:1
Safety Critical Software 5:1 to 1:2
Microsoft Windows 2000 1:2
NASA Space Shuttle Flight Control Software 1:10

Test Point Analysis
• A method with the possibility to perform a
technology-independent measurement of the test
size of an information system on the basis of a
function point analysis, and to use this
measurement as a basis for a productivity
measurement, an estimate of the required
resources, and project management
Software Testing: A Guide to the TMap Approach
Martin Pol, Ruud Teunissen, Erik van Veenendaal

Test Point Analysis (cont.)
For more details on TPA,
look in this book.

Principles of Test Point Analysis
• TPA only considers measurable quality characteristics that fall
within the range of system or acceptance testing
• TPA is (in principle) analyst independent
• TPA depends on the availability of function points
• Sufficient knowledge of the test team is a precondition
• TPA estimates are based on the assumption that, on average,
one complete re-test will be conducted

Team Estimates
• Teams often produce better estimates than
individuals (See The Wisdom of Crowds)
• Team estimates can facilitate buy-in and
commitment from the entire team
• Almost all estimating methods can be done
using teams

The Wisdom of Crowds
Four elements required to form a wise crowd:
• Diversity of opinion
• Independence
• Decentralization
• Aggregation

Test Estimation Sessions ― Wideband Delphi

Test Estimation Sessions ― Wideband Delphi
• Developed by Barry Boehm and John Farquhar
in the 1970s*
• A variant of the existing Delphi method but
with more interaction
• Popularized in Boehm’s book Software
Engineering Economics in 1981
• Similar to the Platinum Poker method used in
some agile projects

Estimation for a new product or technology
To develop an “order of magnitude”
estimate
Requirements are shaky or incomplete
Multiple disciplines are involved
Wideband Delphi

Test Estimation Sessions — Wideband Delphi
Steps from Barry Boehm’s book:
1. Coordinator presents each expert with a specification
and estimation form
2. Experts discuss issues in group meeting
3. Experts fill out form anonymously
4. Coordinator distributes a summary
5. In a group meeting experts discuss variances
6. Experts fill out forms again for as many rounds as
necessary

Wideband Delphi Process Flow
Assemble
estimates
and
assumptions
Estimation
meeting
Individual
preparation
Planning and
kickoff
meeting
Re-estimate
Accept
able
Done
No Yes
Adapted from Karl Wiegers

Sample Estimation Form — Wideband Delphi
Task Estimate #1 Estimate #2 Estimate #3 Estimate #4 Final
Change
Total

Discussion Items:Discussion Items:
What are story points?
q
Measure of size/complexity, not time
q
Relative unit of measure
Is there any relation to function
points or test points?
Estimating Using Story Points ― Agile

• Story points are used for long term or release planning
and tracking
• Point-estimated stories along with team velocity can be
used to provide rough release-level scheduling and
project progress
• Since story points are relative size indicators, a two-
point story is always twice as big as a one-point story

Why Use Story Points?
• Cheaper
• Allow us to change our minds as new
information becomes available
• Don’t take a lot of time
• Foster collaboration
• Consistent
• Provide credibility
• Can use Planning Poker Cards with story points

Planning Poker

Planning Poker
• Based on the Wideband Delphi method espoused by Barry Boehm and
others in the 60s and 70s (and Boehm’s work was possibly based on
earlier works dating to the 40s)
• Most commonly used in agile software development
• A study published by the IEEE in April 2007 indicated that Planning
Poker achieved less optimistic and more accurate estimates than those
obtained through mechanical combinations of individual estimates

Planning Poker Rules
1. Form a group of no more than ten estimators and a moderator. The
product owner is usually present but cannot estimate
2. Each participant gets a deck of cards. The decks frequently use a
doubling sequence (½, 1, 2, 4, 8, 16, 32…) or the Fibonacci sequence (1,
2, 3, 5, 8, 13, 21, 34, 55, 89, 144…) or a “modified” Fibonacci sequence
such as (0, 1, 2, 3, 5, 8, 13, 20, 40, 100)
3. The moderator reads one user story at a time to the team
4. The product owner answers questions about the story

Planning Poker Rules (cont.)
5. Each estimator privately selects a card from his or her deck
representing their estimate of the “size” of the user story
6. When everyone is ready, all of the cards are flipped over at
the same time
7. If there is a consensus, the results are recorded, and the team
moves on to the next story
8. If the estimates vary widely, the owners of the high and low
estimates defend their positions to the rest of the team

Planning Poker Rules (cont.)
9. The group briefly debates the arguments
10. Repeat from step 5 until the estimates converge
11. Repeat for all stories

Planning Poker Rules ― Tips
1. Those who actually could do the work are the ones who vote
2. Managers don’t vote
3. When there is a tie in the voting between two sizes that are
consecutive, just pick the larger size and move on
4. Stop implementation discussions before they go too deep
5. Use an “I need a break” card

Planning Poker Rules ― Tips (cont.)
6. Use a timer to limit discussions
7. If consensus cannot be reached by the end of the third
round of voting, pick the largest size and move on
8. Have the person creating the user stories meet with QA and
Development leads prior to playing poker to answer the
most obvious questions
9. Remember the baseline
10.Have fun!

T-Shirt Sizing
An estimation technique, usually used in agile projects, that uses
relative sizing based upon a limited number of values (e.g., t-
shirt sizes): S, M, L, XL
Steps:
– Make S, M, L, XL cards
– Stories are read one at a time
– Each developer gives each story a t-shirt size
– All developers raise their cards simultaneously
– Discuss differences
– Go back to step 3
– Compile the stories into size buckets
– Estimate the time to complete a S, M, L,XL

Hadden’s Size/Complexity Technique
Rita Hadden
Small Medium Large
Simple
Moderate
Complex
Complexity
Size

Sample Estimate
Test Objective Size Complexity Effort
1 to 3 1 to 3 Sessions 1 2 3
to001 2 1 4 1 1 4 8
to002 2 3 16 2 4 8 16
to003 2 3 16 3 8 16 32
to004 2 1 4
to005 3 1 8
to006 2 2 8
to007 3 2 16
Typical Usage Scenarios 2 2 8
total 80 sessions
20 days
Size
Complexity

Simple Defect Estimation Models
• Defect Detection Percentage (DDP)
• Historical Extrapolation
• “Bug Budgets”

Karl Wiegers’s Estimation Safety Tips
• A goal is not an estimate
• The estimate you produce should be unrelated
to what you think the requester wants to hear
• The correct answer to any request for an
estimate is “Let me get back to you on that”
• Avoid giving single point estimates
• Incorporate contingency buffers into estimates

Rick Craig’s Tips for Better Estimates
• Do it!
• Collect metrics
• Remember the “fantasy” factor
• Don’t “pad” your estimates*
• Don’t spend a ton of time
• Estimates don’t have to be perfect
– Estimates are just estimates
– They will change/constantly as you re-estimate
– Remember planning risks and contingencies
– Remember Brooke’s Law
• If the date is fixed, estimate something else
• Use tools
• Use ranges of value instead of discrete numbers

Robert Sabourin’s Tips for Better Estimates
• Consider estimating the amount of test
coverage possible given a fixed amount
of testing effort
• Present stakeholders with alternatives
(Plan-a,Plan-b,Plan-9)
• Use ranges of values
• Highlight business organizational and
technical factors which impact the
estimate
• Using used envelopes to present rough
estimates

Thank You
• On behalf of SQE Training, we
appreciate your attendance at this
course
• If you have additional training or
consulting needs, please think of us

Brainstorming Testing Estimates
• Min Max Missing
– Identify participants
– Hold kick off meeting
– Individuals build list
– Logging meeting
– Estimate depth of testing
– Estimate size in sessions
– Identify new, missing, or
redundant ideas

BrainstormingTesting Ideas
• Min Max Missing
– PMI Estimation
– Given three values
q
MIN = BEST CASE
q
MAX = WORST CASE
q
EXPECTED = NORMAL CASE
q
Estimate = (MIN + 4*EXPECTED + MAX)/6

Effort Estimation
Reading
Steve McConnell, Rapid Development: Taming Wild
Software Schedules. Chapter 8

Estimating Software Projects
• Duration (on time)
– Schedule time
– Delivery
• Quality (on quality)
– Defect density
– Defect arrival
• Cost (on budget)
– Money
q
Fixed
q
Variable
– Opportunity
q
What else could we be doing?

Estimating Software Projects
• Effort
– Work
– Manpower
• Staffing
– Team definition
– Skills, experiences

Estimation Process
McConnell Basic Steps:
1. Estimate size
2. Estimate effort
3. Estimate schedule

Estimation Process
1 - Estimate Size

Estimation Size
• SLOC
– Source lines of code
• FP
– Function points
– Synthetic measure of software size

Estimation Size
• Software Tools
– Calibration base on relevant experience
– Combine several methods
– Estimator (free!)
q
www.construx.com

Estimation Size
• Analogy
– Similar past projects
– Relative to past experience
– Must take into account
q
Team
q
Technology
q
Complexity
q
Development process maturity

Estimating Size
• Algorithmic
– Function points
– Given requirements determine the number and
complexity of
q
Inputs
q
Outputs
q
Inquiries
q
Logical files
q
External interfaces
– Calculate and adjust based on “type of
project/technology”

Function Points
Low Complexity Medium Complexity High Complexity
Program Characteristic count multiplier count multiplier count multiplier total
Inputs 6 3 2 4 3 6 44
Outputs 7 4 7 5 0 7 63
Inquiries 0 3 2 4 4 6 32
Logical Internal Files 5 7 2 10 3 15 100
External Interface Files 9 5 0 7 2 10 65
Unadjusted Function Point Total 304
Influence Multiplier 1.15
Adjusted Function Point Total 349.6

Function Points ― Influence Factors
1 Data communications How many communication facilities are there to aid in
the transfer or exchange of information with the
application or system?
2 Distributed data processing How are distributed data and processing functions
handled?
3 Performance Did the user require response time or throughput?
4 Heavily used configuration How heavily used is the current hardware platform
where the application will be executed?
5 Transaction rate How frequently are transactions executed daily, weekly,
monthly, etc.?
6 On-line data entry What percentage of the information is entered on-line?
7 End-user efficiency Was the application designed for end-user efficiency?

Function Points ― Influence Factors
8 On-line update How many ILFs are updated by on-Llne transaction?
9 Complex processing Does the application have extensive logical or
mathematical processing?
10 Reusability Was the application developed to meet one or many
user’s (users’) needs?
11 Installation ease How difficult is conversion and installation?
12 Operational ease How effective and/or automated are start-up, back
up, and recovery procedures?
13 Multiple sites Was the application specifically designed,
developed, and supported to be installed at
multiple sites for multiple organizations?
14 Facilitate change Was the application specifically designed,
developed, and supported to facilitate change?

Function Points ― Influence Factors Score
Score Influence
0 Not present, or no influence
1 Incidental influence
2 Moderate influence
3 Average influence
4 Significant influence
5 Strong influence throughout

Function Points Influence Factors Score
INF = 0.65 + SCORE/100

Function Points ― Input
• Input
– Number of screens, forms, dialogues, controls, or
messages through which an end user or another
program adds, deletes, or changes data

Function Points ― Output
• Output
– Screens, reports, graphs, or messages generated for
use by end users or other programs

Function Points ― Inquiries
• Inquiries
– Direct access to data in database

Function Points ― Logical Files
• Logical Files
– Major groups of end user data; could be a “file” or
“database table”

Function Points ― External Interfaces
• External Interface Files
– Files controlled by other applications with which the
program interacts

Function Points ― Advantages
• Advantages include
– Technology independent
– Strong relationship to effort
– Commonly used
• Disadvantages include
– Requires specialized training to
compute

Estimation Process
2 - Estimate Effort

Effort Estimate
• Jones’s First-Order Estimation Practice
• Effort = (FP count) exponent

Jones’s First Order Exponents
Organization Capability
Kind of Software Best Average Worst
Systems 0.43 0.45 0.48
Business 0.41 0.43 0.46
Shrink Wrap 0.39 0.42 0.45

Estimation Process
3 - Estimate Schedule

Estimating Schedule
• Rule of Thumb (McConnell 8-1)
• Schedule = 3.0 * (effort) 1/3

Estimating Schedule
• Software Engineering Past Data
– Example McConnell table 8-8, 8-9
q
LOC vs. Schedule and Effort
q
Different project types

Estimation Convergence
Effort Range Schedule Range
Project Phase Optimistic Pessimistic Optimistic Pessimistic
Initial product definition 0.25 4.00 0.60 1.60
Approved product definition 0.50 2.00 0.80 1.25
Requirement specification 0.67 1.50 0.85 1.15
Product design specification 0.80 1.25 0.90 1.10
Detailed design specification 0.90 1.10 0.95 1.05
Product complete 1.00 1.00 1.00 1.00

Initial
product
definition
Approved
product
definition
Requirements
specification
Product
design
specification
Detailed
design
specification
Product
complete
1.0×
0.25×
4×
2×
0.5×
1.5×
0.67×
1.25×
0.8×
1.0×
0.6×
1.6×
1.25×
0.8×
1.15×
0.85×
1.1×
0.9×
Project Cost (effort and size) Project Schedule
Estimate ― Convergence Graph

More Relationships
Code Volume Approx. 100 LOC/FP, varies widely
[Pressman, p. 94]
Schedule (FP)0.4
Calendar months
Staffing (FP)/100
Average – follows Raleigh curve
Effort Schedule * Staffing = (FP1.4)/150

Rules of Thumb

Rules of Thumb (cont.)
• Experience from several projects
indicates that the amount of testing
required in a project is proportional to
the amount of development required
• Ratio depends on
– History
– Organization, culture, structure
– Projects
– Teams

Rules of Thumb (cont.)
• Hadden’s Size/Complexity Technique
Small Medium Large
Simple
Moderate
Complex
Complexity
Size

Developer/Tester Ratios

Sample Estimate Project A
to001 2 1 4 1 1 4 8
to002 2 3 16 2 4 8 16
to003 2 3 16 3 8 16 32
to004 2 1 4
to005 3 1 8
to006 2 2 8
to007 3 2 16
total 80 sessions
20 days
Size
Complexity

Sample Estimate Project B
to001 1 1 1 1 1 4 8
to002 1 1 1 2 4 8 16
to003 2 1 4 3 8 16 32
to004 2 1 4
to005 2 1 4
to006 2 1 4
to007 1 1 1
to008 1 1 1
to009 3 2 16
to010 2 2 8
to011 2 2 8
to012 2 2 8
to013 2 2 8
to014 3 2 16
to015 3 2 16
to016 2 2 8
total 116 sessions
29 days
Complexity
Size

Sample Estimate Project C
to001 3 2 16 1 1 4 8
to002 1 1 1 2 4 8 16
to003 1 1 1 3 8 16 32
to004 1 1 1
to005 1 1 1
to006 1 1 1
to007 1 2 4
to008 1 2 4
to009 1 1 1
to010 1 2 4
to011 1 2 4
to012 2 2 8
to013 2 2 8
to014 2 2 8
to015 1 1 1
to016 1 1 1
to017 2 1 2
to018 2 2 8
to019 3 2 16
total 98 sessions
24.5 days
Complexity
Size

Sample Estimate Project D
t001 1 1 1 1 1 4 8
t002 1 1 1 2 4 8 16
t003 1 1 1 3 8 16 32
t004 2 1 4
t005 2 1 4
t006 2 2 8
t007 1 1 1
t008 1 1 1
t009 1 1 1
t010 2 1 4
t011 2 2 8
t012 1 1 1
t013 1 2 4
t014 1 2 4
t015 1 2 4
t016 1 1 1
t017 2 1 4
t018 1 1 1
t019 1 2 4
t020 2 1 4
t021 1 1 1
t022 1 1 1
t023 2 2 4
t024 1 1 1
t025 2 1 4
t026 1 1 1
t027 1 1 1
t028 2 1 4
t029 1 2 4
t030 1 2 4
t031 2 1 4
t032 1 1 1
Scenarios 2 2 8
total 99 sessions
24.75 days
Complexity
Size

Test Estimation in Practice

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