University of Southern California
Center for Systems and Software Engineering
Software Sizing
Ali Afzal Malik & Vu Nguyen
USC-CSSE
CSCI 510
10/16/2009
©USC-CSSE
1
University of Southern California
Center for Systems and Software Engineering
Outline
•
•
•
•
•
Nature, value, and challenges of sizing
Sizing methods and tools
Sizing metrics
Maintenance sizing
Conclusions and references
10/16/2009
©USC-CSSE
2
University of Southern California
Center for Systems and Software Engineering
Nature and Value of Sizing
• Definitions and general characteristics
• Value provided by sizing
• When does sizing add less value?
10/16/2009
©USC-CSSE
3
University of Southern California
Center for Systems and Software Engineering
Size: Bigness/Bulk/Magnitude
Measured in gallons
Measured in usable square feet
Measured in ???
10/16/2009
©USC-CSSE
4
University of Southern California
Center for Systems and Software Engineering
Sizing Characteristics
• Generally considered additive
– Size (A U B) = Size (A) + Size (B)
• Often weighted by complexity
– Some artifacts “heavier” to put in place
– Requirements, function points
10/16/2009
©USC-CSSE
5
University of Southern California
Center for Systems and Software Engineering
Software Size is an Important Metric
• A key metric used to determine
– software project effort and cost:
(effort, cost) = f(size, factors)
–
–
–
–
time to develop
staff
quality
productivity
10/16/2009
©USC-CSSE
6
University of Southern California
Center for Systems and Software Engineering
When Does Sizing Add Less Value?
• Often easier to go directly to estimating
effort
– Imprecise size parameters
• GUI builders; COTS integration
– Familiar, similar-size applications
• Analogy to previous effort: “yesterday’s
weather”
• When size is a dependent variable
– Time-boxing with prioritized features
10/16/2009
©USC-CSSE
7
University of Southern California
Center for Systems and Software Engineering
Sizing Challenges
• Brooks’ factors: software system, product
• Cone of uncertainty
10/16/2009
©USC-CSSE
8
University of Southern California
Center for Systems and Software Engineering
Brooks’ Factor of 9 for Programming System Product
x3
A
A
Programming
Program
System
x3
A
A
Programming
Programming
Systems
Product
Adapted from
Brooks, 1995
Product
Product: Handles off-nominal cases, testing; well-documented
System: Handles interoperability, data management, business
management
Telecom: 1/6 basic call processing; 1/3 off-nominals; 1/2 billing
10/16/2009
©USC-CSSE
9
University of Southern California
Center for Systems and Software Engineering
The Cost and Size Cone of Uncertainty
• If you don’t know what you’re building, it’s hard to
estimate its size or cost
Boehm et al. 2000
10/16/2009
©USC-CSSE
10
University of Southern California
Center for Systems and Software Engineering
Outline
•
•
•
•
•
Nature, value, and challenges of sizing
Sizing methods and tools
Sizing metrics
Maintenance sizing
Conclusions and references
10/16/2009
©USC-CSSE
11
University of Southern California
Center for Systems and Software Engineering
Basic Methods, Strengths, and Weaknesses
(Adapted from Boehm, 1981)
Method
Strengths
Weaknesses
Pair-wise comparison
•Accurate assessment of
relative size
•Absolute size of benchmark
must be known
Expert judgment
•Assessment of
representativeness,
interactions, exceptional
circumstances
•No better than participants
•Biases, incomplete recall
Analogy
•Based on representative
experience
•Representativeness of
experience
Parkinson
•Correlates with some
experience
•Reinforces poor practice
Price to win
•Often gets the contract
•Generally produces large
overruns
Top-down
•System level focus
•Efficient
•Less detailed basis
•Less stable
Bottom-up
•More detailed basis
•More stable
•Fosters individual commitment
•May overlook system level costs
•Requires more effort
10/16/2009
©USC-CSSE
12
University of Southern California
Center for Systems and Software Engineering
Comparison with Previous Projects
• Comparable metadata: domain, user base,
platform, etc.
• Pair-wise comparisons
• Differential functionality
– analogy; yesterday’s weather
10/16/2009
©USC-CSSE
13
University of Southern California
Center for Systems and Software Engineering
Group Consensus
• Wideband Delphi
–
–
–
–
–
–
Anonymous estimates
Facilitator provides summary
Estimators discuss results and rationale
Iterative process
Estimates converge after revision in next rounds
Improves understanding of the product’s nature and
scope
– Works when estimators are collocated
• Planning Poker (Cohn, 2005)
–
–
–
–
–
–
–
10/16/2009
Game: deck of cards
Moderator provides the estimation-item
Participants privately choose appropriate card from deck
Divergence is discussed
Iterative – convergence in subsequent rounds
Ensures everyone participates
Useful for estimation in agile projects
©USC-CSSE
14
University of Southern California
Center for Systems and Software Engineering
Probabilistic Methods
• PERT (Putnam and Fitzsimmons, 1979)
–
–
–
–
–
–
3 estimates: optimistic, most likely, pessimistic
Expected Size = optimistic + 4 * (most likely) + pessimistic
6
Std. Dev. = (pessimistic – optimistic) / 6
Easy to use
Ratio reveals uncertainty
Example
Component ai
10/16/2009
mi
bi
Ei
δi
SALES
6K 10K 20K 11K
2.33K
DISPLAY
4
7
13
7.5
1.5
INEDIT
8
12
19
12.5 1.83
TABLES
4
8
12
8
1.33
TOTALS
22
37
64
39
δ E = 3.6
©USC-CSSE
15
University of Southern California
Center for Systems and Software Engineering
Why Do People Underestimate Size?
(Boehm, 1981)
• Basically optimistic and desire to please
• Have incomplete recall of previous
experience
• Generally not familiar with the entire
software job
10/16/2009
©USC-CSSE
16
University of Southern California
Center for Systems and Software Engineering
Outline
•
•
•
•
•
Nature, value, and challenges of sizing
Sizing methods and tools
Sizing metrics
Maintenance sizing
Conclusions and references
10/16/2009
©USC-CSSE
17
University of Southern California
Center for Systems and Software Engineering
Sizing Metrics vs. Time and Degree of Detail
(Stutzke, 2005)
Process
Phase
Possible
Measures
Primary
Aids
Concept
Subsystems
Key Features
Product
Vision,
Analogies
Elaboration
User Roles,
Use Cases
Construction
Screens,
Reports, Function
Components Objects
Files,
Points
Application
Points
Operational
Concept,
Specification,
Context
Feature List
Diagram
Architecture, Detailed
Top Level
Design
Design
Source
Lines of
Code,
Logical
Statements
Code
Increasing Time and Detail
10/16/2009
©USC-CSSE
18
University of Southern California
Center for Systems and Software Engineering
Sizing Metrics and Methodologies
Function Points
COSMIC Function Points
Use Case Points
Mark II Function Points
Requirements
CK Object Oriented Metrics
Class Points
Object Points
Architecture and Design
Source Lines of Code (SLOC)
Module (file) Count
Code
10/16/2009
©USC-CSSE
19
University of Southern California
Center for Systems and Software Engineering
Counting Artifacts
• Artifacts: requirements, inputs, outputs,
classes, use cases, modules, scenarios
– Often weighted by relative difficulty
– Easy to count at initial stage
– Estimates may differ based on level of detail
10/16/2009
©USC-CSSE
20
University of Southern California
Center for Systems and Software Engineering
Cockburn Use Case Level of Detail Scale
(Cockburn, 2001)
10/16/2009
©USC-CSSE
21
University of Southern California
Center for Systems and Software Engineering
Function Point Analysis - 1
• FPA measures the software size by quantifying functionality
of the software provided to users
• Five function types
– Data
• Internal Logical File (ILF), External Interface File (EIF)
– Transactions
• External Input (EI), External Output (EO), External Query (EQ)
• Each function is weighted by its complexity
• Total FP is then adjusted using 14 general systems
characteristics (GSC)
EO/EO/EQ
Huma
n User
EI/EO/EQ
MS Word
MS Excel
EIF
(system being counted)
EO/EO/EQ
ILF
MS Outlook
EIF
10/16/2009
©USC-CSSE
22
University of Southern California
Center for Systems and Software Engineering
Function Point Analysis - 2
• Advantages
– Independent of technology and implementation
– Can be measured early in project lifecycle
– Can be applied consistently across projects and organizations
• Disadvantages
– Difficult to automate
– Require technical experience and estimation skills
– Difficult to count size of maintenance projects
10/16/2009
©USC-CSSE
23
University of Southern California
Center for Systems and Software Engineering
COSMIC FP
• COSMIC measures functionality of the software thru the
movement of data (exit, entry, read, write) cross logical layers
• Advantages
– Same as FPA
– Applicable to real-time systems
• Disadvantages
–
–
–
–
Difficult to automate
Require experience and estimation skills
Require detailed architecture
Difficult to understand for customers
10/16/2009
©USC-CSSE
24
University of Southern California
Center for Systems and Software Engineering
Use Case Point
• UCP counts functional size of the software by
measuring the complexity of use cases
UCP = (#Actors + #Use cases) * technical and environmental factors
• Advantages
– Independent of technology and implementation
– Can be measured early in project lifecycle
– Easier to count than FPA
• Disadvantages
– Lack of standardization
– Difficult to automate
– High variance (low accuracy)
10/16/2009
©USC-CSSE
25
University of Southern California
Center for Systems and Software Engineering
CK Object-Oriented Metrics
• Six OO metrics (Chidamber and Kemerer, 1994)
–
–
–
–
–
–
Weighted Methods Per Class (WMC)
Depth of Inheritance Tree (DIT)
Number of Children (NOC)
Coupling Between Object Classes (CBO)
Response for a Class (RFC)
Lack of Cohesion in Methods (LCOM)
• Advantages
– Correlated to effort
– Can be automated using tools
• Disadvantages
– Difficult to estimate early
– Useful only in object-oriented code
10/16/2009
©USC-CSSE
26
University of Southern California
Center for Systems and Software Engineering
Source Lines of Code (SLOC)
• SLOC measures software size or length by counting the logical or
physical number of source code lines
– Physical SLOC = number of physical lines in code
– Logical SLOC = number of source statements
• Advantages
–
–
–
–
Can be automated easily using tools (e.g., USC’s CodeCount)
Easy to understand for customers and developers
Reflect the developer’s view of software
Supported by most of cost estimation models
• Disadvantages
– Dependent on programming languages, programming styles
– Difficult to estimate early
– Inconsistent definitions across languages and organizations
10/16/2009
©USC-CSSE
27
University of Southern California
Center for Systems and Software Engineering
SLOC Counting Rules
•Standard definition for counting lines
- Based on SEI definition checklist from CMU/SEI-92-TR-20
- Modified for COCOMO II
S ta te m e n t ty p e
In c lu d e s
1 . E x e c u ta b le

E x c lu d e s
2 . N o n -e x e c u ta b le :
10/16/2009
3.
D e c la ra tio n s

4.
C o m p ile r d ire c tiv e s

5.
C o m m e n ts :
6.
O n th e ir o w n lin e s

7.
O n lin e s w ith s o u rc e c o d e

8.
B a n n e rs a n d n o n -b la n k s p a c e rs

9.
B la n k (e m p ty ) c o m m e n ts

10.
B la n k lin e s

©USC-CSSE
Adapted from
Madachy, 2005
28
University of Southern California
Center for Systems and Software Engineering
Relationship Among Sizing Metrics
• Two broad categories of sizing metrics
– Implementation-specific e.g. Source Lines of
Code (SLOC)
– Implementation-independent e.g. Function
Points (FP)
• Need to relate the two categories
– e.g. SLOC/FP backfire ratios
10/16/2009
©USC-CSSE
29
University of Southern California
Center for Systems and Software Engineering
SLOC/FP Backfiring Table
(Jones, 1996): other backfire ratios up to 60% higher
10/16/2009
©USC-CSSE
30
University of Southern California
Center for Systems and Software Engineering
Multisource Estimation
Implementation-independent/dependent
• Implementation-independent estimators
– Use cases, function points, requirements
• Advantage: implementation-independent
– Good for productivity comparisons when using VHLLs,
COTS, reusable components
• Weakness: implementation-independent
– Gives same estimate when using VHLLs, COTS, reusable
components, 3GL development
• Multisource estimation reduces risk
10/16/2009
©USC-CSSE
31
University of Southern California
Center for Systems and Software Engineering
Outline
•
•
•
•
•
Nature, value, and challenges of sizing
Sizing methods and tools
Sizing metrics
Maintenance sizing
Conclusions and references
10/16/2009
©USC-CSSE
32
University of Southern California
Center for Systems and Software Engineering
Sizing in Reuse and Maintenance
Release 1
Release 2
Reused Code
Acquired Code
Project Execution
Adapted Code
Exiting System’s
Code
New Code
10/16/2009
©USC-CSSE
33
University of Southern California
Center for Systems and Software Engineering
Calculating Total Equivalent SLOC - 1
• Formulas:
EKSLOC
EKSLOC
AAM
AAM
reused
adapted
reused
adapted
 KSLOC
 KSLOC
 0 . 3 IM
reused
adapted
* AAM
* (1 
reused
AT
100
) * AAM
adapted
reused
AAF 2

AA

AAF

[
1

(
1

) ] * SU * UNFM

100

if AAF  100

100

AA  AAF  SU * UNFM
if AAF  100

100

AAF  0 . 4 * DM  0 . 3 * CM  0 . 3 * IM
DM = % design modified, CM = % code modified, IM = % integration and test needed
10/16/2009
©USC-CSSE
34
University of Southern California
Center for Systems and Software Engineering
Calculating Total Equivalent SLOC - 2
EKSLOC
10/16/2009
 KSLOC
added
 EKSLOC
©USC-CSSE
adapted
 EKSLOC
reused
35
University of Southern California
Center for Systems and Software Engineering
Adaptation Adjustment Modifier (AAM)
1.60
AAM w orse case:
AA = 8
SU = 50
UNFM = 1
1.40
Relative Cost
1.20
Old AAM f ormula
New AAM f ormula
1.00
0.80
0.60
Relative # of interf aces
checked
0.40
0.20
0.00
0
20
40
60
80
100
120
Relative Amount of Modification (AAF)
10/16/2009
©USC-CSSE
36
University of Southern California
Center for Systems and Software Engineering
Conclusions
• Size plays an important role in estimation and
project management activities
– Software estimation is a “garbage in garbage out”
process
• Bad size in; bad cost out
• A number of challenges make estimation of size
difficult
• Different sizing metrics
– FP, COSMIC FP (cfu), Use Case Points, SLOC, CK OO
metrics
• A plethora of sizing methods exists each method
has strengths and weaknesses
10/16/2009
©USC-CSSE
37
University of Southern California
Center for Systems and Software Engineering
References
•
Books
–
–
–
–
–
–
–
•
Journals
–
–
•
Chidamber, S. and C. Kemerer, 1994, “A Metrics Suite for Object Oriented Design,”
IEEE Transactions on Software Engineering.
Putnam, L.H., and Fitzsimmons, A., 1979. Estimating software costs. Datamation.
Tutorials/Lectures/Presentations
–
–
–
–
•
Boehm, Barry W., Software Engineering Economics, Prentice Hall, 1981.
Boehm, Barry W., et al., Software Cost Estimation With COCOMO II, Prentice Hall,
2000.
Brooks, Jr., Frederick P., The Mythical Man-Month, Addison-Wesley, 1995.
Cockburn, A., Writing Effective Use Cases, Addison-Wesley, 2001.
Cohn, M., Agile Estimating and Planning, Prentice Hall, 2005.
Jones, C., Applied Software Measurement: Assuring Productivity and Quality,
McGraw-Hill, 1996.
Stutzke, Richard D., Estimating Software-Intensive Systems, Addison-Wesley, 2005.
Boehm, Barry W., and Malik, Ali A., COCOMO Forum Tutorial, “System & Software
Sizing”, 2007.
Boehm, Barry W., CSCI 577a Lecture, “Cost Estimation with COCOMO II”, 2004.
Madachy, Ray, CSCI 510 Lecture, “COCOMO II Overview”, 2005.
Valerdi, Ricardo, INCOSE Presentation, “The Constructive Systems Engineering
Cost Model”, 2005.
Websites
–
–
–
10/16/2009
http://www.crisp.se/planningpoker/
http://www.ifpug.org/
http://www.spr.com/
©USC-CSSE
38
Descargar

Tutorial: System and Software Sizing