SDPM - Lecture 5 - Software effort estimation

Leiden Institute of Advanced Computer Science

System s Development and Project
Management –
Software effort estimation

Prof. Dr. Thomas Bäck

1

Leiden Institute of Advanced Computer Science Dates

Feb. 1 14:45 – 17:30 Introduction, Project Description
Feb. 2 13:45 – 16:30 STEP WISE Approach to Project Planning
Feb. 9 13:10 – 15:45 Selecting an Appropriate Software Dev.
Approach
Feb. 15 14:45 – 17:30 Activity Planning and Resource Allocation
Feb. 16 15:15 – 18:00 Software Effort Estimation
Feb. 22 14:45 – 17:30 Risk management, project escalation
Feb. 23 13:45 – 16:30 Project monitoring and control
Mar. 1 14:45 – 17:00 Exam
Mar. 2 13:45 – 16:30 Software Quality Assurance
Mar. 8 14:45 – 17:30 Managing People; Contract Management
Mar. 9 13:45 – 16:30 Various
Mar. 15 14:45 – 17:30 Trade Fair

2


STEP WISE overview
1. Identify project objectives 0. Select Project 2. Identify project infrastructure

3. Analyze pr. characteristics

4. Identify products and activities
Review lower
level detail
5. Estimate effort for activity
For each activity

6. Identify activity risks

10. Lower level planning 7. Allocate resources

9. Execute plan 8. Review / publicize plan

3


What makes a successful project?

Delivering: Stages:
!   Agreed functionality 1. Set targets
!   On time 2. Attempt to achieve
!   At the agreed cost targets
!   With the required quality

But what if the targets are not achievable?

4


Software effort estimation:
Notoriously difficult …
!   Complexity and invisibility of software
!   Intensely human activities of system development
!   Cannot be treated in a purely mechanistic way
!   Novel applications of software
!   Changing technology
!   Lack of homogeneity of project experience

5


The Cone of Uncertainty
4x

1.5x Detailed Design
Complete
2x
1.25x Requirements
1.0x complete
0.8x
0.67x Software complete
User Interface Design
Complete
0.5x
Approved product
definition

0.25x
Initial concept

Time

6


TAXONOMY OF METHODS

7


Over and under-estimating
!   Parkinson s Law: Work expands to fill the time
available
!   An over-estimate is likely to cause project to take
longer than it would otherwise
!   Brook s Law: Putting more people on a late job
makes it later.
!   Weinberg s Zeroth Law of reliability: a software
project that does not have to meet a reliability
requirement can meet any other requirement
!   If you don t care about quality, you can meet any
other requirement
8

-  Only 50% kept project data on past projects - but 60.8% used analogy!
-  35% did not produce estimates
-  62% used methods based on intuition - only 16% used formalized methods

Effort estimation: Taxonomy -  Function point users produced worse estimates!
Expert-estimation 25.5%
Effort
estimation Analogy 60.8%
methods „Capacity problem“ 20.8%
Price-to-win 8.9%
Algorithmic/ Empirical Parametric models 13.7%
Parametric
Heemstra & Kuster

Function Expert-
Points COCOMO Price-to-win Parkinson Percentage-
Analogy-based based estimation

Data Points COCOMO II Delphi

Object Points PERT

Why do we need them ?
Web Points •  Complexity and invisibility of software Single person
estimate
•  Intensely human activities of system
development
Use Case •  Cannot be treated in a purely mechanistic way
Points
•  Novel applications of software
•  Changing technology

Leiden Institute of Advanced Computer Science Overview
Method Complexity Pros Cons
Function Points High Standardized, Requires data
transparent foundation,
partially subjective
COCOMO II Average to high Good for first raw System size
estimate, tool estimate required
support available
Expert-estimation Small to average Quick, flexible Subjective,
missing
transparency
Analogy-based Small to average Quick for similar Not applicable to
projects new projects, data
foundation
required
Percentage-based Small Applicable early Requires data
on, after initial foundation,
phase variance for new
projects


A taxonomy of estimating methods

!   Expert opinion - just guessing?
!   Bottom-up: activity based
!  Components are identified and sized
!  Estimates are aggregated
!  More appropriate at later, more detailed stages of
project planning
!   Parametric: e.g. function points
!  Use effort drivers representing characteristics of the
target system and the implementation environment to
predict effort

11


A taxonomy of estimating methods (cont d)
Not recommended
!   Analogy
!  A similar, completed project is identified, and its actual
effort is used as the basis for the new project
!   Artificial neural networks - a view of the future?
!   Parkinson: based on staff-effort available
!  Cf. Parkinson s law – use staff effort available
!   Price to win : figure sufficiently low to win
contract
!  Estimate what the client s budget is
Not recommended
12


Top-down vs. bottom-up Hours per KLOC

!   Top-down (usually parametric models)
!  Produce overall estimate based on project cost
drivers
!  Based on past project data
!  Normally formulas such as
effort = system size x productivity rate
!  FP focuses on system size
!  COCOMO focuses on productivity factors
!   Bottom-up
!  Use when no past project data is available
13


Top-down estimating

Estimate !   Produce overall
Overall
100 days estimate using effort
project
driver(s)
!   Distribute proportions of
Design Code Test overall estimate to
components
30% 30% 40%
i.e. i.e. i.e. 40 days
30 days 30 days

14


Bottom-up estimating

1. Break project into smaller and smaller
components
[2. Stop when you get to what one person can
do in one/two weeks]
3. Estimate costs for the lowest level activities
4. At each higher level calculate estimate by
adding estimates for lower levels

15

Leiden Institute of Advanced Computer Science Empirical Methods

Price-to-win •  Figure sufficiently low to win contract

•  Based on staff-effort available
Parkinson •  Parkinson’s Law: “Work expands to fill the time available”

•  A similar, completed project is identified, and its actual effort is
Analogy-based used as the basis for the new project

Percentage- •  Top-down approach, based on overall effort estimate
•  Distribute percentages of effort to components in work
based breakdown structure

Expert- •  Delphi: Multiple estimates
•  Single expert estimate
estimation •  PERT: Schedule risk estimation approach


Algorithmic/parametric methods
Function •  Focus on system size/
points functionality

COCOMO •  Focus on productivity factors

!   Parametric = top-down
!  Produce overall estimate based on project cost
drivers
!  Based on past project data
!  Normally formulas such as
effort = system size / productivity rate (KLOC/h)


PARAMETRIC MODELS

18


Parametric Models

!   Simplistic model:
estimated effort = (system size) / productivity rate

!   E.g.:
!  System size = lines of code (KLOC)
!  Productivity rate = KLOC/day
!   How to derive productivity rate?
productivity rate= (system size) / effort
!  Based on data from past projects


Basic approach

Function points COCOMO

•  Used to estimate •  Focus on productivity
system size (SLOC, •  Lines of code as
source lines of code) input

Number of I/O transaction types

System System
Model size size Model Effort

Number of file types
Productivity factors


Parametric models

!   COCOMO (lines of code) and function points
examples of these
!   Problem with COCOMO etc:

Guess Algorithm Estimate

but what is desired is…

System
Algorithm Estimate
characteristic

21


Parametric models (cont d)

!   Examples of system characteristics
!  No. of screens x 4 hours
!  No. of reports x 2 days
!  No. of entity types x 2 days
!   The quantitative relationship between the
input and output products of a process can be
used as the basis of a parametric model

22


Parametric models (cont d) KLOC per hour

!   Simplistic model for an estimate
estimated effort = (system size) / productivity
rate
!   E.g.
!  System size = lines of code
!  Productivity = lines of code per day
!   Productivity rate = (system size) / effort
!  Based on past projects

23


Productivity Focus:

COCOMO

24


COCOMO: Constructive Cost Model

!   Based on industry productivity standards -
database is constantly updated
!   Allows an organization to benchmark its
software development productivity
!   Basic equation: effortnom = m * sizen
!  Person-months
!  Thousands of delivered source code instructions
(kdsi)
!   Refers to a group of models

25


COCOMO: Constructive Cost Model

!   System types:
!  Organic (small teams, in-house software
development, small and flexible systems)
!  Embedded (tight product operation constraints; costly
changes)
!  Semi-detached (combines elements of the above;
intermediate)

26


COCOMO: Example

!   System types:
!  Organic: m=2.4, n=1.05, o=2.5, p=0.38
!  Semi-detached: m=3.0, n=1.12, o=2.5, p=0.35
!  Embedded: m=3.6, n=1.20, o=2.5, p=0.32
!   Effort = m * size[kdsi]n
!   Time = o * effortp = o * (m * sizen)p
!   Example: Organic project, 1,500 dsi
!  Effort = 2.4 * 1.51.05 = 3.67
!  Time = 2.5 * 3.670.38 = 4.1

27


COCOMO (cont d)
!   Intermediate version of COCOMO incorporates 15
cost drivers,
!  Product attributes:
•  Required software reliability, database size used, product
complexity.
!  Computer attributes:
•  Execution time constraint, main storage constraint, virtual
machine volatility, computer turnaround time.
!  Personnel attributes:
•  Analyst capability, application experience, programmer
capability, virtual machine experience, language experience
!  Project factors:
•  Modern programming practice, software tools, development
schedule.

28


COCOMO (cont d)

!   Complementary equation:
Effortest = Effortnom * dem1 * … * dem15
(demi: development effort multiplier)
!   Effortnom as before (i.e., exponential function)

29


System Size Focus:

FUNCTION POINTS

30


System size: Function Points (FP)

!   Based on work at IBM 1979 onwards
!  Albrecht and Gaffney wanted to measure the
productivity independently of lines of code
!  Has now been developed by the International FP
User Group (which is US based)
!  Mark II FPs developed by Simons mainly used in
UK
!   Based on functionality of the program

31


Albrecht function points external interface files

internal
external logical
inputs files external
outputs
•  Based on program functionality
•  2 data function types
•  3 transactional function types
•  Each occurrence is judged simple,
average, or complex

external inquiries
• Input transaction through screens, forms, dialog boxes.
External input types • Updates internal files

External output types • Data is output to user by screens, reports, graphs, control signals.

• Standing files used by system
Logical internal files • One or more record types (group of data that is usually accessed together)

External interface files • Input and output passing through and from other computer applications

• Transactions initiated by user
External inquiry types • Provide information, but do not update internal files
• User inputs some information that directs system to details required


If judged …
Albrecht FP weightings
… then assign
… points
Type Simple Average Complex

ILF 7 10 15

EIF 5 7 10

EI 3 4 6

EO 4 5 7

EQ 3 4 6

33


IFPUG developments
Functional complexity was later defined by rules e.g.
internal logical files and external interface files as below:

Record
types <<< Data Types >>>
1-19 20-50 > 50
1 simple simple average
2-5 simple average complex
>5 average complex complex

34


IFPUG external input complexity

File
<5 5-15 > 15
1 simple simple average

35


IFPUG external output complexity

File
1-19 20-50 > 50
0 or 1 simple simple average

36


IFPUG external inquiry complexity

!   External inquiries are counted both as if they
were an external input and an external output.
!   Use higher score of the two.

37

Leiden Institute of Advanced Computer Science Assignment 3
EI EO ILF EIF EQ
2 FTR: LECTURER, DEP, 4 0 0
1 Low: 3
DETs 31 :3 = 10.3
1 FTR: LECTURER, 3 DETs 0 1 FTR:
2 34 :5 = 6.8
Low: 3 Low: 3
LECTURER,
3 DETs
4 (all 1 0
3 FTR: TEACHING, 0 0
3 RET, < 20
High: 6
LECTURER, COURSE), 11
DET (do not count
data 34 :6 = 5.6
types)
activity_ref, staff_id,
course_code)
4 x Low: 7
3 FTR: LECTURER, 0 1 FTR:
4 High: 6
COURSE, TEACHING_ACT, = 28 Low: 3
TEACHING_ 37 :4.5 = 8.2
11 DETs ACT, 7 DET
4 FTR, 12 DETs 0 2 FTR:
5 High: 6 Low: 3
(LECTURER, 37 :7.72 = 4.8
COURSE), 2
DET

38


From FPs to LoCs
Language Minimum (minus 1 Mode (most Maximum (plus 1 s.-
s.-dev.) common) dev.)
C 60 128 170

C# 40 55 80

C++ 40 55 140

Cobol 65 107 150

Fortran 90 45 80 125

Fortran 95 30 71 100

Java 40 55 80

SQL 7 13 15

MS Visual Basic 15 32 41

After: McConnel, Steve: Software Estimation, Microsoft Press, Redmond, WA, 2006, p. 202.

39


Mark II function points

!   Developed by Charles R. Symons
!   Software sizing and estimating - Mark II
FPA , Wiley & Sons, 1991.
!   Builds on work by Albrecht
!   Work originally for CCTA:
!  Should be compatible with SSADM; mainly used in
UK
!   Has developed in parallel to IFPUG FPs

40


Mark II function points (cont d)

For each TA count:
No. entities !   Data types input (Ni)
accessed !   Data types output (N
o)
!   Entity types accessed
(Ne)

Technical complexity
No. input No. output adjustments (TCA)
data items data items

FP count = Ni * 0.58 + Ne * 1.66 + No * 0.26
41


Mark II function points (cont d)
!   Weightings derived by asking developers
!  Which effort has been spend in previous projects
!  … regarding processing inputs
!  … regarding processing outputs
!  … regarding accessing and modifying stored data.
!   Work out average hours of work
!   Normalize averages into ratios or weightings
which add up to 2.5 Adjustment to
!   … or: use industry averages. Albrecht FPs

42


Using Mark II function points

!   Calculate FPs for each transaction in a
system
!   Total transaction counts to get a count for the
system
!   Recall that estimated effort = size (FPs) x
productivity rate (effort per FP)
!   Productivity rate obtained from past projects

43


ANALOGY BASED

44


Estimating by analogy: case-based
reasoning Use effort
Source cases from source as
estimate
attribute values effort
attribute values effort Target case
attribute values effort attribute values ?????

Select case
with closest attribute
values
45


Estimating by analogy (cont d)

!   Identify significant attributes ( drivers )
!   Locate closest match amongst source cases
for target
!   Adjust for differences between source and
target

46


Code-Oriented Approach

!   Envisage number / types of modules in final
system.
!   Estimate SLOC of each identified program
!  Implementation language specific
!   Estimate work
!  Take into account complexity and technical
difficilty.
!   Calculate work-days effort

47


Anchor and adjust
Pace distance
N
on a bearing

Church with
steeple

Forest
go to church
by line of sight
Forest

You are here:
how do you get to
red cross?
48


Machine assistance for source selection
(ANGEL)
Source A

Source B
Number of inputs

It-Is

Ot-Os
Target

Number of outputs
Euclidean distance = sq root ((It - Is)2 + (Ot - Os)2 )
49


Stages: identify

!   Significant features of the current project
!   Previous project(s) with similar features
!   Differences between the current and previous
projects
!   Possible reasons for error (risk)
!   Measures to reduce uncertainty

50


CONCLUSIONS

51


Some conclusions: how to review
estimates
Ask the following questions about an estimate
!   What are the task size drivers?
!   What productivity rates have been used?
!   Is there an example of a previous project of
about the same size?
!   Are there examples of where the productivity
rates used have actually been found?

52


Strenghts and Weaknesses

!   Expert judgement:
!  Expert with relevant experience can provide good
estimation.
!  Fast estimation.
!  Dependent on the „expert“.
!  May be biased.
!  Suffers from incomplete recall.

53



!   Analogy:
!  Based on actual project data and past experience.
!  Similar projects may not exist.
!  Historical data may not be accurate.

!   Parkinson, price-to-win:
!  Often win the contract.
!  Poor practice.
!  May have large overruns.
54



!   Top-Down (parametric, FP, COCOMO):
!  System level focus
!  Faster and easier than bottom-up
!  Require minimal project detail.
!  Provide little detail for justifying estimates.
!  Less accurate than the other methods.

55



!   Bottom-Up:
!  Based on detailed analysis.
!  Support project tracking better than other methods.
!  Estimates address lower level tasks.
!  May overlook system level cost factors.
!  Requires more estimation effort than top-down.
!  Difficult to perform the estimate early in the life cycle.

56



!   Algorithmic (FP, COCOMO):
!  Objective, repeatable results.
!  Gain a better understanding of the estimation method.
!  Subjective inputs.
!  Calibrated to past projects and may not reflect the
current environment.
!  Algorithms may be company specific and not be
suitable for software development in general.

57


Heemstra and Kuster s survey (cont d)

!   Only 50% kept project data on past projects -
but 60.8% used analogy!
!   35% did not produce estimates
!   62% used methods based on intuition - only
16% used formalized methods
!   Function point users produced worse
estimates!

58

SDPM - Lecture 5 - Software effort estimation

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