3 Estimation

SoberIT
Software Business and Engineering Institute

Previous lecture: Processes…

HELSINKI UNIVERSITY OF TECHNOLOGY

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Process Choice
  There are several factors Risks ~
that affect which type of -  novelty of
process you should use: methods,
tools,
  Product type application
  Business type Iterative, prototyping
domain
  Project size - fuzziness of
requirements
  Project initial Incremental
uncertainty
  Environmental
uncertainty
  Organizational culture Plan-driven
  …
Product
size/complexity


Dimensions Affecting Process Model
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Selection and Engineering Institute
Software Business
Personnel (Boehm & Turner 2003)

(Percent level 1B) (Percent level 2 and 3)
40 15

30 20

20 25
Criticality Dynamism
(Loss due to impact (Percent requirements
on defects) Single 10 30 change/month)
life
Discretionary
Many 1
funds 0 35 5
lives
10
Essential
funds 30
Comfort 50
3
90 Ag
10 ile
70
Pla
30 n-d
r ive
50 n
100
Size 30
300 Culture
(# of
(% of thriving on chaos vs. order)
personnel) 10
Size Culture
(Number of personnel) (Percent thriving on chaos versus order)

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T-76.5612 Software Project Management
Spring 2010

3: Software estimation

Tuomas Niinimäki

Department of Computer Science and Engineering
Helsinki University of Technology


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What is estimation?


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What is estimation?
In mathematics, use of a function or formula to derive a solution or
make a prediction.

Unlike approximation, it has precise connotations. In statistics,
for example, it connotes the careful selection and testing of a
function called an estimator. In calculus, it usually refers to an
initial guess for a solution to an equation, which is gradually
refined by a process that generates closer estimates. The
difference between the estimate and the exact value is the error.

(Encyclopedia Britannica Concise)


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Why would we want to
estimate software?


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The use of estimates (1/3)
Phase / Activity Why The question answered
Strategic Planning Finding out potential Could we do this more efficiently than
application domains the others?
Prioritizing projects Should we emphasize this project over
the others?
Feasibility study Cost-benefit analysis, Is the project worth doing?
making a bid

(Adapted from Boehm, 2000, Hughes & Cotterell, 2002)

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Evaluation of Evaluating project cost How much should we pay for this
suppliers’ project?
proposals
Evaluating supplier’s Is this bidding realistic?
proposals
Has the supplier understood the
requirements?

Project Planning Budgeting How much is the project going to cost?
Resourcing and How long is the project going to take?
Scheduling
How does the resourcing decisions affect
it?


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System Requirements analysis How long does it take to implement a
specification feature/requirement?
Which features will take the most effort?
Project execution Iteration-level plans How much effort do the different tasks
take?
Making trade-offs Should I leave this feature out to match
the schedule wishes?


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What to estimate in software
project?


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project?

Effort

Schedule Size


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project?
Resources

Effort

Schedule Size
Time Scope

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What can be estimated in software?
  Software size
  Lines of code?
  Number of classes / methods?
  Amount of database tables / queries / user views?
  Functionality?

  Effort
  Person-hours

  Schedule
  Calendar-days or months


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Derived estimates (examples)
  Software size
  Potential / probable number of defects
  Probable number of change requests
  …

  Effort
  Project cost
  Team size

  Schedule
  Suitable number of iterations / milestones


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Project cost drivers (from COCOMO81)

… or even better,

Use the

relevant
cost drivers

for

your project /organization


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How to estimate software?
  Estimation process in a nutshell:

1 2 3
Estimate the size of the product Estimate the effort Estimate the schedule

  Provide the estimates in ranges
  Periodically refine the ranges to provide increasing precision as
the project progresses
(McConnell, Rapid Development, 1996)


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Problems in software estimation (1/6)
  Basic problem: Predicting the future by looking into the
past

HELSINKI UNIVERSITY OF TECHNOLOGY http://www.flickr.com/photos/wabbit42/3691518084/ CC BY-NC-SA 2.0

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  A lack of good historical information
  Does the historical information have such metrics that distinguish the
previous projects from each others?
  Does the historical data contain enough cases to make a reliable
estimation?
  Can these metrics describe the project you are estimating?
  Do we have the right metrics to measure the project environment?


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  A lack of information on the project to be estimated
  Most influential decisions are made in the early phases of
project, based on inadequate information
  Important information is not available
  ... or there simply is not enough time to collect it


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  Estimates are done sloppily
  ”If they cannot be done perfectly, why pay attention to
them?”
  There can be a order-of-magnitude difference between a
careful and a sloppy estimate
  There are no “final estimates” - they can (and should!) be
updated whenever new information becomes available


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Project Cost Project
(effort and size) schedule

4× 1.6×

2× 1.25×

1.5× 1.15×
1.25× 1.1×
1.0× 1.0×
0.8× 0.9×
0.67× 0.85×

0.5× 0.8×

0.25× 0.6×
Initial Approved Requirements Product Detailed Product
product product specification design design complete
HELSINKI UNIVERSITYdefinition
definition OF TECHNOLOGY specification specification

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  Estimates are not updated
  Estimating is done only in the beginning of the project, based
on incomplete information
  New estimates are not done, but the old estimates are still
followed

  As new information becomes available, estimates should be
updated
  Estimates can be used in various stages of software project
  e.g. size estimation for estimating the number of bugs


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  Estimates are not followed, respected or trusted
  An estimate should not be an opinion
  An opinion can be overruled by your superior
  An estimate is not automatically a commitment
  ... but may be considered as such
  Justify and criticize the estimates
  Accept uncertainties
  Visualize probabilities


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Estimation in practice


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Tools for estimation
  Work breakdown structure
  Product breakdown structure
  Top-down estimation
  Bottom-up estimation


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Work breakdown structure
  Work Breakdown Structure (WBS) is a graph (a tree) of activities
done during the project
  Each activity is broken down into tasks, until desired level of
detail is reached
  A task must contain work related only to their parent activity
  The top-level activity (= project) must contain 100% of work
in the project
  For each node, the sum of work shares in its sub-activities
must be 100%
  A desired level of detail for tasks is usually the size of task
that can be allocated to a single person
  Maximum size is usually 10-20 hours

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Example: Work breakdown structure
Project

Specification Implementation Testing Delivery

Eliciting Implementing Testing Installing
requirements module A module A software
Analyzing Implementing Testing
Training
requirements module B module B
Writing req. Implementing Testing
specification doc. module C module C
Creating Integrating Integration
architecture plan modules testing
Acceptance
testing


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Product breakdown structure
  Product Breakdown Structure (PBS) is a similar construct as
WBS, but is based on the structure of product rather than the
type of activities
  Lower levels in PBS describe the structure of item on level above
  The top level of PBS is the product
  Second level may consist of the major components of the
product
  Lowest level should contain items that are “comprehensible”


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Example: Product breakdown structure

Product

Documentation Software

Project plan Module A

Requirements
Class A_x Module B
specification
Class A_y
Architecture
Class A_z Module C
specification

User manual Main module


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Top-down estimation
  Idea: Allocate proportions of an effort estimate to different
activities of the project
  Inputs: Main project activities (high level design) and
earlier project data on efforts spent for activities
  Outputs: Rough work breakdown with corresponding effort
estimates
  Advantages
  Takes into account integration, configuration management and
documentation costs (e.g. algorithmic models seldom take)
  Disadvantages
  Underestimating the difficult low-level technical problems


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Example: Top-down estimation
100 %
800 h
Project


Training
Acceptance
testing


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100 %
800 h
Project

20 % 40 % 30 % 10 %
Specification h
160 Implementation h
320 Testing 240 h Delivery 80 h

Training
Acceptance
testing


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100 %
800 h
Project

20 % 40 % 30 % 10 %
Specification h
160 Implementation h
320 Testing 240 h Delivery 80 h

50 % Eliciting Implementing Testing Installing
80 h
30 % Analyzing Implementing Testing
48 h requirements Training
module B module B
10 % Writing req. Implementing Testing
16 hspecification doc. module C module C
20 % Creating Integrating Integration
16 h architecture plan modules testing
Acceptance
testing


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Bottom-up estimation
  Idea: Calculate the total effort from the sum of effort
estimations of single tasks (by analogy estimation or
expert judgement)
  Inputs: Detailed work breakdown (tasks 1-2 weeks for 1
person), descriptions of factors affecting production
  Outputs: Effort estimates on individual tasks, total effort
  Advantages
  If the task division is accurate, the model may provide the most
accurate estimates
  Underestimates costs of system level (e.g. integration and
documentation)
  Needs detailed breakdown of project tasks


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Example: Bottom-up estimation
Project


Eliciting 60 Implementing
h Testing Installing
Training
Acceptance
testing


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Project


Analyzing 80 Implementing
h Testing
Training
Writing req. 45 Implementing
h Testing
Creating 68 h Integrating Integration
Acceptance
testing


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Project

252 h

Analyzing 80 Implementing
h Testing
Training
Writing req. 45 Implementing
h Testing
Creating 68 h Integrating Integration
Acceptance
testing


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695 h
Project

272 h 252 h 100 h 70 h

68 h Eliciting 60 Implementing
h 20 h Testing Installing
10 h
68 h Analyzing 80 Implementing
h 20 h Testing 60 h
Training
68 h Writing req. 45 Implementing
h 20 h Testing
68 h Creating 68 h Integrating 20 h Integration
20 h Acceptance
testing


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Estimation techniques


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Estimation techniques
  Estimation by analogy
  Expert judgment
  Agile estimation
  Algorithmic models
  Albrecht & MarkII function points
  COCOMO 81 and COCOMO II


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Estimation by analogy (1/2)
  Idea: Finding similar cases to the target project
  Inputs: Earlier projects, each described with p features
  Outputs: Project size, effort estimate, schedule
estimate, (estimation error)


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Example: Estimation by analogy

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Project time
tracking
software 41 246 246 287 820 6.3 1 140 8300 Very low Nominal Very high
Customizable
user register
module 25 50 138 37 250 3.2 1 40 2300 Low High High
Mobile
calendar and
email
application 525 1575 700 700 3500 12 3 310 22000 Nominal Very low Nominal
Intranet
product 237 316 553 474 1580 11 1 260 18200 Low High Nominal
Web survey
tool 105 126 84 105 420 2 2 92 5200 Nominal High Low

  For new project:
  Lines of code?
  Complexity? Finding similar Deriving estimate
(inter/extrapolation)
  Platform experience? earlier projects
from actual outcomes

  Domain experience?

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Estimation by analogy (2/2)
  Advantages
  If done systematically, the estimate can be justified to project
stakeholders
  If a good analogy is found, the estimation can be fairly accurate
  Tool support (e.g. ANGEL, http://dec.bournemouth.ac.uk/ESERG/ANGEL/)
  The feature data on earlier projects is limited
  There may not be an analogous project in the project data set
  Choosing the right features for finding analogous project
  The significance of many features only becomes visible afterwards 
It might not be documented systematically even in earlier projects
  By definition, no project is exactly equivalent


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Expert judgment (1/4)
  Idea: Ask the required estimate from persons that have previous
experience on some parts of the problem
  Inputs: Descriptions of problem domain, technology or other
affecting factors
  Outputs: Project size, effort estimate, schedule estimate (in all
varying metrics)

  Dominant technique for effort estimation (Jorgensen, 2004)


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  Who are the experts?
  Your own developers
  Domain experts
  Technology experts
  Other project managers
  Non-technical people may be better at estimating than technical
people
  Skilled technical personnel tend to be overoptimistic
  Varying background (employment, education, domain
experience) of estimators can improve the estimates
  Different backgrounds yield different estimation strategies


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  In practice expert judgment estimates are derived from
analogies to earlier projects
  Experience on similar projects is crucial
  Require justification for estimates, so that they can be
reviewed


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  Advantages
  Cheap and fast
  Easy to adapt to changing situations (a judgement can be made
based on whatever information is available)
  If developers are making the estimates, they are more committed to
them
  More accurate than algorithmic methods, if the experts have good
domain knowledge and the project is of low uncertainty
  The analogies or evaluation criteria cannot be easily made visible 
How to distinguish good estimates from bad ones?
  Underestimation of large tasks and overestimating small
  Can be strongly biased by information that is irrelevant for the
estimates (e.g. schedule constraints)


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Agile estimation (Scrum) (1/5)
Strategic Release Management

Release Project Cycle 3 months

Sprint 1 month

  Time-paced (heartbeat - sprint - release project - strategic)
  Time is fixed, scope can change
  User requirements come as “user stories”
  “Things to do” (tasks) are stored in product & sprint backlog


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  Each feature (a “user story”) is assigned a number of points
  This number represents how hard / time consuming it is to
complete this feature
Story A Story B Story C Story D
3 pt 5 pt 4 pt 7 pt

  What do these numbers mean?
  Where do these numbers come from?


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  They do not mean much individually (they are relative)
  They come out from team “consensus”
  Delphi method, planning poker
3 pt 5 pt 4 pt 7 pt

I think B is much
more work than A
(B = ~2 x A)
I guess A would be quite
I agree, but I think D
simple to do (A = 3)
is the hardest one
(D ~ A + B)


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Planning poker
1.  At the start of planning poker, each estimator is given a
deck of cards.
  Each card has written on it one of the valid estimates (e.g.
1,2,3,5,8,13,…)
2.  For each user story or theme to be estimated, a
moderator reads the description.
  The product owner answers any questions that the
estimators have.
3.  After all questions are answered, each estimator
privately selects a card representing his or her
estimate. Cards are not shown until each estimator has
made a selection
  If estimates differ, the high and low estimators explain their
estimates.
4.  After the discussion, each estimator re-estimates by
selecting a card.
5.  Continue the process as long as estimates are moving
closer together http://www.flickr.com/photos/tomnatt/ CC BY-NC 2.0


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  After a few sprints
  The story points assigned to the user stories start to stabilize
  You may start counting the story points implemented per
sprint
  This measure is called velocity
  Velocity tells how many story points can be completed on
average during a sprint
  However, velocity is not a team performance metric
Sprint 1: planned scope = 8 pt

3 pt 5 pt 4 pt 7 pt


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  Velocity tells how many story points can be completed on
average during a sprint
  Velocity can be used to estimate the amount of work for a
new sprint
Backlog: sum of points = 11 pt > 8 pt

Story C Story D
4 pt 7 pt

Sprint 1: implemented => velocity = 8 pt Sprint 2: “maximum” story points = 8 pt

Story A Story B
3 pt 5 pt


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Agile estimation: Summary
  Advantages
  Simple and comprehensible process
  Easy to set up and maintain
  Empowers developers (helps in team&project commitment)
  Self-calibrating
  Needs active participation, self-organization, motivation
  It can be difficult to give reasoning to the story points
  It usually takes a number of iterations to stabilize
  Long-term estimation is difficult
  Stories are created
  and story points assigned “just-in-time”


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Algorithmic models
  Based on an algorithm (procedure of calculation)
  Model based on (non-)linear regression
  Research on software engineering
  Weights and factors derived from past project metrics

  Function point methods are used to estimate software size
  Constructive cost model (COCOMO) is used to estimate effort
based on estimated software size
  See course material (Estimation of Software) for more details
and examples


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Function point methods (1/2)
  Idea: Quantify the size of programs independently of programming
languages
  Function point methods are based on historical data
  Mathematical model is similar as in (algorithmic) estimation by
analogy, but the granularity is finer
  Function point methods should be calibrated to each organization
  Even though they give one number, they are not necessarily more
(or less) accurate than other estimation techniques

  Inputs: ”External user types” (Albrecht) or ”transactions” (MarkII)
  Outputs: Project size (in function points)


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Function point methods (2/2)
  Advantages
  Can be made in early stages of project (e.g. based on requirements or a
detailed problem description)
  Independent of implementation details or the person estimating
  Tool support
  Difficult to apply for application domains which are hard to describe with
external user types or transactions
  Do not take development process, organization or technology into account
(Apply corrective multipliers for deriving person-hours from function points,
e.g. COCOMO or data on earlier projects)
  Need to be calibrated for each organization
  May give false sense of accurate estimates


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Albrecht function points (1/3)
  Based on five major components - the external user types
  External input type: input transactions from user that update internal
files
  External output type: transactions that output data to user
  Logical internal file type: data storage used by the system
  External interface file type: input/output between different computer
systems
  External inquiry type: transactions initiated by users that do not
update the internal files


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  Function points are calculated for each activity in the software,
including
  functionality
  reports
  data storage
  The total function points for a system is the sum of function
points for all activities


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  Steps to calculate function points for an activity:
1. Identify the external user type for the activity
2. Identify the record types used in the activity
  Record types could be modeled as classes in OOP
  Note that you might need to take a look at other activities to fully
determine the record types used in this activity
3. Identify the data types
  Data types could be modeled as attributes in OOP
4. Determine component complexity multiplier based on external user
type and the amount of record and data types involved
  Identifying record and data types requires training to do
properly, and can be subjective in some cases


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MarkII function points
  MarkII function points are calculated for activities modeled as
transactions:
  Input data is data provided by the user
  The system then processes the input data, and (possibly)
manipulates some internal data (= entity types)
  The system outputs data to user
  Calculating the total function points for the system is done by
calculating the sum of function points of the transactions in the
system


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Constructive cost model (COCOMO)
  Idea: Quantify the effort required based on a size estimate
  Inputs: ”Lines of code” (COCOMO 81 & II) or ”function
points” (COCOMO II), ”cost multipliers”, ”scale factors”
  Outputs: Effort estimate (in person-months = 152 person-hours)

  COCOMO II dataset is updated continuously, software
support is available
  Possibly the biggest and most descriptive dataset on software
projects


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  The basic model of COCOMO 81 is based on the equation
effort = c x sizek
  effort = person-months
  size = thousands of delivered source code instructions
  c,k = constants (depend on the system type)
  System types are classified as follows:
  Organic mode: relatively small team, highly familiar environment,
flexible interface requirements
  Embedded mode: very tight constaints on the system
  Semi-detached mode: characteristics between organic and embedded
mode


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  The intermediate model of COCOMO 81 takes development
environment into account via 15 cost drivers, resulting in
equation
effort’ = effort x dem = c x sizek x dem
  effort’ = effort estimate adjusted by cost drivers
  effort = original effort estimate from the basic model
  dem = development effort multiplier
  The 15 cost driver coefficients are multiplied to get the
development effort multiplier
  Each cost driver is evaluated on scale (very low, low, nominal, high,
very high, extra high)
  Values for each cost driver are determined from past projects


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  Advantages
  Independent of the estimator (provided that the scale factors can be
determined objectively)
  An effort estimate can be derived quickly if the needed information is
available
  Underlying assumption that lines of code or function points are easier
to calculate than the effort needed
  A company needs a huge dataset of its own projects to calibrate all
the scale factors
  Risk: you get ”an exact number”


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What techniques are actually used?
Technique McAulay Heemstra Wydenbach
1987 1989 1995
Expert Expert Judgement 26% 86%
based Intuition and experience 85% 62%

Estimation by Analogy 61% 65%

Model
Algorithmic Models 13% 14% 26%
based
Other Price-to-win 8% 16%

Capacity related 21% 11%

Top-down 13%

Bottom-up 51%

Other 12% 9% 0%

(Moløkken et al.: A Survey on Software Estimation in the Norwegian Industry, 2004)


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Effort estimation best practices (1/3)
  Allow time for making the estimate, and plan it
  Use documented data from previous projects
  Use developer-based estimates
  Estimate by walk-through
  Estimate at a low level of detail
  Don’t omit common tasks
  Use software estimation tools
  Use several different estimation techniques, and compare the
results
  Monitor and adapt estimation practices as the project progresses

(McConnell, 1996)


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  Reduce situational and human bias
  Find estimation experts with highly relevant domain background and
good estimation records
  Ask the evaluators to justify and criticize their estimations

  Assess the uncertainty of the estimate
  Use plus/minus qualifiers or value ranges to indicate the direction of
uncertainty or estimation range
  Quantify risks – explicate the reasons of uncertainty

  Provide estimation feedback and training opportunities
  Provide feedback on estimation accuracy and development task
relations
  Provide estimation training opportunities
(Jørgensen, 2004)


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  Use several estimation techniques and compare them
  If they converge, you are probably on the right track
  Find out why the estimates are different
  Delphi method - Combine several expert opinions
  Ask each expert to make an estimate independently
  Then let each of them present their estimate and reasoning
  Based on this discussion, let them adjust their estimation
  Iterate until the estimates converge
  Ask several different estimates – optimistic, probable and
pessimistic – and compare them


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Exercise 1 best practices
  Start early!
  Read the exercise carefully!
  Read the additional material (especially Estimation of Software)
  Estimation is sometimes subjective
  Don’t get frustrated!
  Sometimes you just have to assume
  Remember to give reasoning and make your process visible
  Each question is graded separately
  Don’t give up!
  Keep track of the time you are spending and give feedback!


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Thank you.

Questions?
Tuomas Niinimäki

tuomas.niinimaki@soberit.hut.fi


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Examples


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Example: Albrecht function points
  Actual requirement from exercise 1:
List category products: This screen lists the product, its name, price, short description,
thumbnail image,variations (size, colors and materials) and their stock availability.
When clicking a product name or image, a detailed product data screen is activated
(see FR3). The user is also able to make an order through this screen (see FR4).
  Step 1: Identify the external user type
  User type is external inquiry, as no data structures are updated.
  Step 2: Identify record types
  Category, Product, Customer, Discount
  Note that you may have to look into other requirements to identify
all records that are present - in this case, the Discount type is stated
in a different requirement (not shown here).
  Step 3: Identify data types
  categoryName, productName, price, shortDescription, smallImage,
size, color, material, availability


SoberIT

Example: Albrecht function points
  Step 4: Determine component complexity multiplier based on
external user type and the amount of record and data types
involved
  External inquiry with 4 record types, 9 data types
  External inquiries use whichever complexity is higher,
external input or external output complexity
  In this case, both give a high complexity
  For our requirement, the multiplier for high external
inquiry complexity is 6; therefore, we get 6 adjusted
function points for this requirement!


SoberIT

Example: MarkII function points
  Actual requirement from exercise 1:
List category products: This screen lists the product, its name, price, short
description, thumbnail image,variations (size, colors and materials) and their
stock availability. When clicking a product name or image, a detailed product
data screen is activated (see FR3). The user is also able to make an order
through this screen (see FR4).
  Step 1: Identify input types, entity types and output types
  Input types: categoryName
  Entity types: Category, Product, Customer, Discount
  Output types: productName, price, shortDescription, smallImage,
size, color, material, availability
  Step 2: Calculate function point amount using weights
  0.58*1 + 1.66*4 + 0.26*8 = 9.30 function points.


SoberIT

Example: Constructive cost model
  Using COCOMO 81 to estimate effort:
1. Calculate function points for the system
  Example: FP = 19
2. Calculate source lines of code for the system based on the function
points
  Example: Development in Java, thus SLOC = 60 x 19 = 1140
3. Apply the basic model for nominal effort estimate
  Example: Organic model (c=2.4,k=1.05) => effort = c x sizek =
2.4 x (1.140)1.05 = 2.75 person-months
4. Apply the development effort multiplier
  Example: Other cost drivers at nominal level, but programmer
capability (PCAP) and operating system experience (VEXP) is
high: dem = 0.80 x 0.90 = 0.72 => effort’ = 0.72 x effort =
1.98 person-months


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